Create an end-to-end AI solution that will help predict insurance premium cost with IBM Watson Studio and AutoAI.

Machine Learning based model to predict Insurance Pure Premium

Predictive Modeling for Insurance Premiums Using Machine Learning" "Risk Assessment and Classification in the Insurance Industry" "Automated Underwriting: Enhancing Risk Evaluation with Machine Learning" "Data-Driven Insights for Vehicle Insurance Pricing" "Machine Learning for Claims Cost Estimation and Fraud Detection"

Insurance Premium Prediction is an Machine Learning Project which predicts Insurance premium price based on some Input data.

Provider Fraud is one of the biggest problems facing Medicare. According to the government, the total Medicare spending increased exponentially due to frauds in Medicare claims. Healthcare fraud is an organized crime which involves peers of providers, physicians, beneficiaries acting together to make fraud claims. Rigorous analysis of Medicare data has yielded many physicians who indulge in fraud. They adopt ways in which an ambiguous diagnosis code is used to adopt costliest procedures and drugs. Insurance companies are the most vulnerable institutions impacted due to these bad practices. Due to this reason, insurance companies increased their insurance premiums and as result healthcare is becoming costly matter day by day. Healthcare fraud and abuse take many forms. Some of the most common types of frauds by providers are: a) Billing for services that were not provided. b) Duplicate submission of a claim for the same service. c) Misrepresenting the service provided. d) Charging for a more complex or expensive service than was actually provided. e) Billing for a covered service when the service actually provided was not covered. Problem Statement The goal of this project is to " predict the potentially fraudulent providers " based on the claims filed by them. Along with this, we will also discover important variables helpful in detecting the behavior of potentially fraud providers. further, we will study fraudulent patterns in the provider's claims to understand the future behavior of providers. About the dataset : For the purpose of this project, we are considering Inpatient claims, Outpatient claims and Beneficiary details of each provider. Lets s see their details : A) Inpatient Data This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code. B) Outpatient Data This data provides details about the claims filed for those patients who visit hospitals and not admitted in it. C) Beneficiary Details Data This data contains beneficiary KYC details like health conditions, Regio region they belong to etc.

Analysing insurance customer data to predict the premium paying ability

Predicted clv making it easier for the auto-insurance companies to decide on the premiums of their incoming clients and thus balance the total risk in the market

Statistical analysis and prediction of medical insurance premium based on beneficiary's health & lifestyle (regression modelling in R)

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This project aims to predict property insurance premiums using machine learning techniques. The dataset consists of various features related to insurance policies and properties. The goal is to build models that can accurately predict the annual premium based on these features.

Deep learning model for predicting insurance premiums using PyTorch for Kaggle's Regression with an Insurance Dataset competition ↓

Predict health insurance premiums with machine learning! This project aims to develop a model that can accurately estimate health insurance premiums based on various factors. this project provides insights into the complexities of health insurance pricing.

Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company. An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee. For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else. Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer. Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue. Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

ClaimWise is a machine learning project designed to predict whether an insurance customer will file a claim based on customer demographics, vehicle characteristics, and driving history. By building intelligent models, the project helps insurance companies assess risks more accurately, optimize premium pricing, and improve decision-making.

This project offers an intuitive web application for nano-entrepreneurs to predict health insurance costs. Leveraging a machine learning model, it estimates expenses based on personal factors, aiding financial planning. Built with Flask and scikit-learn, it provides a user-friendly interface for quick and accurate insurance premium estimation.

Predict Your Medical Insurance Premium !

It predicts the Health Insurance Premium depending upon individual health condition

This project aims to predict the insurance premium for individuals based on their demographic and medical information.

A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Insurance companies apply numerous techniques for analysing and predicting health insurance costs

This app was livestreamed on youtube from beginning to end. This app predicts customers car insurance premium and customer lifetime value to insurance companies.

Health-Insurance-Premium-Predictor

A Streamlit app predicting health insurance premium costs.

A FastAPI-based insurance premium predictor deployed on AWS

A web application that predicts health insurance premium using Random Forest Regression algorithm.

A web app that predicts the insurance premium charges of an individual using Machine learning

PremiumPredictor — FastAPI + Streamlit app that predicts health insurance premium categories using a scikit‑learn model.

This project aims to predict the insurance premium for individuals based on their demographic and medical information.

The goal of this project is to develop a predictive model that can accurately estimate the medical insurance premium for potential policyholders. By accurately predicting premiums, the insurance company can provide more precise quotes to customers, leading to better customer satisfaction and improved financial planning for the company.

Your client is an Insurance company and they need your help in building a model to predict  whether the policyholder (customer) will pay next premium on time or not.   An insurance policy is an arrangement by which a company undertakes to provide a guarantee of  compensation for specified loss, damage, illness, or death in return for the payment of a specified  premium. A premium is a sum of money that you pay regularly to an insurance company for this  guarantee.   For example, you may pay a premium of Rs. 5000 each year for a medical insurance cover of Rs.  200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance  provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are  wondering how can company bear such high hospitalisation cost when it charges a premium of  only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you,  there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a     few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone  shares the risk of everyone else.   Just like medical insurance, there is life insurance where every year you pay a premium of certain  amount to insurance provider company so that in case of unfortunate event of your death, the  insurance provider company will provide a compensation (called ‘sum assured’) to your  immediate family. Similarly, there can be a variety of insurance products for different kinds of  risks.  As you can imagine, if a large number of customers do not pay the premium on time, it might  disrupt the cash flow and smooth operation for the company. A customer may stop making  regular premium payments for a variety of reasons - some may forget, some may find it expensive  and not worth the value, some may not have money to pay the premium etc.   Building a model to predict whether a customer would make the premium payment can be  extremely helpful for the company because it can then accordingly plan its communication  strategy to reach out to those customers who are less likely to pay and convince them to  continue making timely payment.  Now, in order to predict, whether the customer would pay the next premium or not, you have  information about past premium payment history for the policyholders along with their  demographics (age, monthly income, area type) and sourcing channel etc.  

Predicted individual health insurance costs using supervised ML models on demographic and health-related features. Enabled personalized premium estimation through EDA, preprocessing, and model evaluation.