Insurance claim prediction dataset This project focuses on predicting whether policyholders will file a claim in the next six months using a comprehensive dataset. This is due to the fact that frauds or claims are rare events when Feb 28, 2024 · For the dataset used, XGBoost resulted in the best prediction of an efficient insurance claim process for customer behavior and could be used for better anticipation of claim amounts. The models were tested recursively and average predictive results were compared controlling for false positives and false negatives. Thus, it wastes healthcare financial resources and increases healthcare costs. This dataset serves as a foundation for training machine learning models capable of forecasting medical expenses for new policyholders. Insurance Claim Prediction Machine Learning. For this project, we recommend using the sample dataset provided in the data folder. The dataset used contains information such as age, sex, BMI, number of children, smoking statu Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Explore the "Insurance Claims and Policy Data" dataset, a comprehensive collection designed to facilitate predictive analytics and risk assessment within the insurance industry. 4157, selected for test dataset predictions and competition submission. The dataset contains comprehensive information about insurance policies, policyholders' demographics, vehicle details, claim history Insurance Claims Fraud Detection This project aims to predict insurance claims fraud using machine learning algorithms. Travel Insurance Claims Prediction Goals The goal of this project is to determine if we can predict the claim status (Yes or No) from the various travel insurance-related attributes. a contract within an insurance company and an individual (policyholder). It showcases Snowflake's Feature Store and Model Registry, emphasizing streamlined ML operations tailored for the insurance claims domain. Several factors affect insurance claim charges, which are taken into consideration when developing insurance policies. The dataset used in this project was sourced from Kaggle and contains information about customer demographics, travel details, and whether an insurance claim was filed. Policyholder Prediction & Classification of Insurance Claim dataset Insurance Prediction dataset has 1. generating realistic synthentic data representing 3 years of medical insurance claims, in order to build a ML model that will predict if a claim will be denied or approved (yes/no) as well as the probability. Discover how Synthesized uses data manipulation tools to eliminate imbalanced datasets, for high-quality vehicle insurance claim predictions. The dataset used in this project is obtained from Kaggle and contains information about insurance claims, including policy details, incident details, and whether the claim was fraudulent or not. Jul 29, 2024 · 3. Dec 1, 2024 · Based on actuarial science theory, decision-making theory, and anonymous big data, this study employs machine learning to advance insurance claim fore… The dataset, titled "Vehicle Insurance Fraud Detection," includes 15,421 instances of insurance claims with the following key components: Claim Information: Time-related details such as month, week, and day. Description of Dataset: This is the "Sample Insurance Claim Prediction Dataset," which is based on the " [Medical Cost Personal Datasets]. Abstract Each year, almost 10% of claims are denied by payers (i. Using a dataset from Kaggle; provided by AllState, a US-based insurance company; the training dataset consists of 130 attributes (features) and the loss value for each observation. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural Abstract The insurance industry, with its large datasets, is a natural place to use big data solutions. The dataset used contains information such as age, sex, BMI, number of children, smoking statu Apr 12, 2022 · The insurance industry, with its large datasets, is a natural place to use big data solutions. Download the dataset from the provided link Dataset: The dataset used in this project is sourced from a comprehensive insurance claims dataset, which includes features such as claim amount, claimant information, incident details, and more. In the field of computational and applied mathematics, machine learning (ML) is a well-known research Tweedie regression on insurance claims # This example illustrates the use of Poisson, Gamma and Tweedie regression on the French Motor Third-Party Liability Claims dataset, and is inspired by an R tutorial [1]. Insurance-claim-prediction This code has been written for the Kaggle competition to detect the severity of insurance claims. - Sarah-2510/Vehicle-Insurance-Claim-Prediction Kaggle Submission - Insurance Claim Prediction. The goal of this competition is to better predict Insurance claim payments based on the certain characteristics. Mar 1, 2024 · Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. Although this study is based on a single dataset, the findings provide valuable perspectives on enhancing prediction accuracy and improving risk management practices in the insurance About This project revolves around building a predictive model to estimate car insurance claim outcomes based on various factors related to policyholders. Income: Policyholder's income. Jul 1, 2020 · A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. It helps testing new regression models in those prob-lems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Through preprocessing and hyperparameter tuning, LightGBM attains the best validation MAE of 0. The Car Insurance Prediction project leverages machine learning algorithms to predict insurance claims for policyholders. - GitHub - dmarulan/Predicting-Ins Jul 8, 2020 · Sen Hu and Adrian O’Hagan investigate how cluster analysis with copulas can improve insurance claims forecasting ABSTRACT One of the main challenges facing the insurance companies is to determine the proper insurance premium for each risk represented by customers. Traditional models often struggle with complex and unbalanced data. As a result, classification models tend to have a limited ability to predict the occurrence of claims. Policyholders wish (or are in many cases obligated) to protect themselves against … Aug 16, 2023 · The dataset is accessible via a GitHub repository, highlighting features like 'months_as_customer', 'age', and 'policy_number'. The main focus is the 'fraud_reported' variable, which indicates claim legitimacy. Custom YOLO11m model for detecting and classifying car body damage (99% shattered glass, 96% flat tire detection accuracy)—optimized for high-capacity inference and assistive use Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. With increasing power of storage and computation technologies, insurance companies worldwide are looking Furthermore, our study illustrates the advantages of using machine learning algorithms in handling large and complex datasets, making predictions on future insurance claims, and adapting to changing circumstances, making it a valuable tool for practitioners in the insurance industry. 8L observations – 6 Numeric columns and 12 factor columns. Feb 20, 2021 · The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. GANs Insurance datasets, which are often used in claims severity and claims frequency mod-elling. Health insurance has become a vital aspect of people's lives. The aim is to leverage advanced machine learning techniques to enhance car insurance pricing strategies by accurately predicting claim frequencies. Claims should be carefully evaluated by the insurer, which may take time. The usage of a dataset of medical insurance claims and demographic information to train and evaluate the models. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Historical data is classified into two classes, 0 and 1. This project aims to provide actionable insights for insurance companies to Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. In this dataset, each sample corresponds to an insurance policy, i. Insurance companies have business problems, such as risk assessment, classification of policy holders and resource allocation, insurance claim classification and prediction in the insurance claim handling process [3]. We worked on this dataset as a part of our final group project in a graduate course on Statistical Learning that we took at the University of Waterloo in which we reproduced the results of a paper¹ Sep 27, 2023 · PDF | Automobile insurance fraud is a significant issue for insurance firms, causing financial losses and higher premiums for policyholders. The dataset includes features such as age, gender, smoking status, number of children, diabetic status, and any other relevant information. So, in this paper, we'll use various data level approaches to Add this topic to your repo To associate your repository with the insurance-claim-prediction topic, visit your repo's landing page and select "manage topics. Content Columns age: age of primary beneficiary sex: insurance contractor gender, female, male bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, This project focuses on predicting whether policyholders will file a claim in the next six months using a comprehensive dataset. With the cost to recover these denials and underpayments, pre-dicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs’ performance productivity and drive better patient financial experience and Insurance Claim Prediction using Machine Learning This project uses machine learning to predict whether a policyholder will make an insurance claim based on key demographic and health factors such as age, gender, BMI, smoking status, and medical costs. Nov 29, 2020 · Analytics Vidhya Making an Insurance Claims Prediction model with CatBoost in R Harish Nagpal Follow 6 min read Dec 17, 2024 · This paper seeks to leverage deep neural networks for predicting insurance claims by the automobile customers based on their characteristics and past behavior. e. Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. We applied one of the largest datasets of healthcare insurance claims (over 50 million members), achieving one of the highest performance results reported in the literature reviewed here. Dec 15, 2024 · The present work addresses the challenges of an imbalanced dataset in the context of insurance claim prediction for autos. Jun 28, 2022 · Insurance businesses use ML to provide clients with accurate, quick, and efficient health insurance coverage. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency Aug 5, 2024 · Enabling Explainable AI in Auto Insurance Claim Prediction to improve customer satisfaction, and protecting companies from fraudulent Insurance claims. This predictive model predicts the dataset from auto insurance either the claims is fraudulent or not. As a result, I need to build a predictive model that can predict the probability that a particular claim will be approved immediately or not based on historical and anonymous data. The goal of this project is to predict insurance claim status (whether a claim will be approved or denied) based on customer and policy information. - sh Finally, we would like to build a model that can predict severity of claims so as to improve the claims service to ensure a worry-free customer experience. Nov 29, 2024 · Predicting the severity of insurance claims is a vital task for insurers, enabling them to manage risk and optimize resource allocation effectively. Jul 23, 2025 · Medical Insurance Price Prediction using Machine Learning in Python In this article, we will try to extract some insights from a dataset that contains details about the background of a person who is purchasing medical insurance along with what amount of premium is charged to those individuals as well using Machine Learning in Python. Here, we developed a deep neural network to predict future cost from health insurance claims records. Available features include driver age, vehicle age, vehicle power, etc. Dataset Card for Medical Insurance Cost Prediction The medical insurance dataset encompasses various factors influencing medical expenses, such as age, sex, BMI, smoking status, number of children, and region. Based on the prediction data, the model are able to estimate the total predicted fraudulent claims (amounts), and break down the features The purpose of this project is to determine the contributing factors and predict health insurance cost by performing exploratory data analysis and predictive modeling on the Health Insurance dataset. Insurance policies aim to minimize or reduce the costs incurred due to different risks. machine learning (ML). The dataset, sourced from Kaggle, underwent minimal data cleaning and formatting procedures to ensure its suitability for analysis. This project focuses on predicting the likelihood of car insurance claims based on various customer, vehicle, and policy characteristics. However it must be stressed, that significant number of applications for machine learning in insurance industry, like fraud detection or claim prediction, deals with the problem of machine learning on an imbalanced data set. Sep 15, 2021 · This paper evaluated fraud prediction in property insurance claims using various machine learning models based on real-world data from a major Brazilian insurance company. Abstract Processing insurance claims is a complex task that requires accurate predictions to speed up approvals, reduce fraud, and improve customer satisfaction. This study adopted a sequential model in Keras and compared the model with ReLU and Swish as activation functions. , health insurance plans). Inaccuracies in car insurance claim predictions usually raise its cost for good drivers and reduce the price for bad ones. Subsequently, fraud poses a substantial financial challenge. Predicting Insurance Claim Frequency: A Case Study in Classification on Imbalanced Data This project focuses on predicting the frequency of insurance claims using a dataset containing information about insurance policies, vehicles, and customers Mar 1, 2024 · In this paper, three ensemble ML models, XGBoost, GBM, and RF were deployed for medical insurance cost prediction using the medical insurance cost dataset from KAGGLE's repository. Using a dataset of features from policyholders, vehicles, and demographic information, I developed a machine learning model with a Random Forest Classifier to classify the claim status, enabling better risk assessment and strategic premium pricing for insurers. Risk differs widely from clients to another, and a Careful understanding of various risk factors assists predict the likelihood of insurance claims based on historical data, Real-world datasets often have missing values, can cause bias in Ensemble machine learning project tutorial in python. Jul 2, 2017 · The objective of this work is to predict the severity loss value of an insurance claim using machine learning regression techniques. Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. It excels in handling high-dimensional data and is robust against overfitting, making it well-suited for insurance claim prediction tasks where the dataset may contain numerous input features and a relatively small number of samples. The dataset consists of 116 categorical features (each one named with a generic nomenclature of 'cat'+str (i)) and 15 continuous features (these too names in a similar fashion). Available features include driver age Sample Dataset: You will need a dataset that includes insurance-related data, such as policyholder information, claim details, and relevant features for prediction. Jul 19, 2023 · The Dataset is extracted from the Kaggle. However, methods leveragingthe medical richness from data such as health insurance claims or electronic health records are missing. Prediction of the occurrence of claims in auto insurance has been employed in the present work. Policyholder Sep 23, 2023 · The presence of extensive historical datasets presents an opportunity for data mining to revolutionize underwriting in the life insurance sector. Aug 18, 2024 · The objective of this project is to make a machine learning model using Logistic Regression to predict whether Insurance Claim is Fraud or not. The high dimensional data used for this research work is obtained from Allstate insurance company which consists of 116 categorical Apr 7, 2025 · Predicting insurance claims using IBM SPSS Modeler and CRISP-DM This project aims to predict whether a policyholder is likely to make an insurance claim, using personal, health, and lifestyle-related attributes. csv", is a comprehensive collection of insurance claim records. A study to forecast the stock exchange by applying mac All of these datasets are in the public domain but simply needed some cleaning up and recoding to match the format in the book. This is due to the fact that frauds or claims are rare events when compared This project aims to predict insurance charges based on various factors using machine learning models. I will perform analysis to understand the data, and learn something as to which variables are correlated with a claim being Oct 23, 2023 · Objective The objective of this project is to predict the likelihood that a policyholder will file a claim in the next six months, based on factors that are included in the dataset that pertain to Jan 4, 2022 · Under all three test choices, On the Insurance Claim dataset, the Random Forest model outperforms the other two algorithms, while Nave Bayes outperforms the other two algorithms on the Premium dataset. Abstract—Predicting the frequency of insurance claims has become a significant challenge due to the imbalanced datasets since the number of occurring claims is usually significantly lower than the number of non-occurring claims. health insurance claims datasets from 2017 to 2019. Because health insurance plans typically have access only to their own claims data and not to Dec 31, 2024 · By addressing the challenge of zero-inflation in automobile claim data, this study offers insights into improving the accuracy of claim frequency predictions. Explore the data & deliver key business Insights Fit a regression model with highest adjusted R-square and least RMSE Perform classification on the same dataset & achieve high accuracy scores Perform clustering on the dataset and arrive at Sep 23, 2023 · The presence of extensive historical datasets presents an opportunity for data mining to revolutionize underwriting in the life insurance sector. Each row represents an individual claim, and the columns represent various features associated with that claim. This study | Find, read and cite all the research Discover how Databricks empowers data science in insurance claims, offering tools and insights for efficient claims processing and risk detection. " Learn more In this project we will use the Porto Seguro Safe Driver Prediction dataset from Kaggle to predict the likelihood that a driver will initiate an insurance claim. Feb 22, 2021 · The Dataset 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 This repository features code for the Allstate Claims Severity Kaggle competition, utilizing Python, primarily XGBoost, and LightGBM for predicting insurance claim losses. In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis. The dataset contains various features related to policyholder demographics, vehicle specifications, and policy details, along with a binary target variable is_claim indicating whether a claim was made. By analyzing multiple features related to the policyholder and their vehicle, the model predicts both the likelihood of a claim occurring (classification) and the severity of the claim (regression). The dataset, sourced from the CAS datasets by Christophe Dutang, focuses on motor insurance policy data and claims frequency. Develop a predictive model to estimate the numerical value of an insurance claim based on demographic and health-related features. Oct 30, 2020 · A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions to form a final prediction. " This paper represents a machine learning-based health insurance prediction system. Gender: Policyholder's gender. Jun 24, 2025 · About This project focuses on predicting whether a customer will make a car insurance claim using a real-world dataset from Kaggle. In this article, we present a complete pipeline Abstract Nowadays, data is extremely important and valuable in the insurance sector. A machine learning model to predict the Denial Reason for insurance claim - MacHu-GWU/Denial-Reason-Prediction-Model A model was built to predict the total insurance claim amount payable by the insurance company using machine learning techniques such as regression in python. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. Machine learning (ML) is one of the methods that solves this problem. Aug 21, 2023 · The dataset, named "insurance_claims. However it must be stressed, that signi cant number of applications for machine learning in insurance industry, like fraud detection or claim prediction, deals with the problem of machine learning on an imbalanced data set. The data have many different auto insurance policies with many characteristics of each driver; the training data include whether or not they have filed a claim. This solution provides an end-to-end workflow for predicting insurance claim outcomes using machine learning, focusing on feature engineering, model training, and deployment. S. This amount needs to be included in the yearly financial budgets Explore expert strategies in insurance claim prediction for Industry Associates by a Data Science Manager using DataCalculus insights. Jun 17, 2025 · Several studies utilized diverse datasets and machine learning models to enhance predictive accuracy in claims processing and analysis, such as [16], which established a model to predict high-cost claims using U. By leveraging advanced analytics and machine learning, the project aims to revolutionize how insurance companies assess risk and determine pricing strategies. Contribute to arjhuang/kaggle-porto-seguro development by creating an account on GitHub. The insurance dataset contains information on policyholders including their age, gender, BMI, region, smoking status, and medical costs. com (Car Insurance Claim Prediction | Kaggle) I recommend you to install required software’s like Python 3. Jan 10, 2021 · In this two-part series, we will describe our experience of working on the Prudential Life Insurance Dataset to predict the risk of life insurance applications using supervised learning algorithms. Each record in the dataset represents an individual's health insurance charges along with corresponding demographic and health-related characteristics. May 1, 2023 · Deep learning (DL) models have outperformed traditional Machine Learning (ML) models in multiple domains; despite this, they are underutilized in insurance risk pricing. Insurance-Claim-Prediction Dataset Link: Risk varies widely from customer to customer, and a deep understanding of different risk factors helps predict the likelihood and cost of insurance claims. - cachatj/predicting-insurance-claim-approvals The insurance dataset can be classified into different categories of details like policy details, claim details, party details, vehicle details, repair details, risk details. The dataset represents a sample of first-party physical damage claims referred to Travelers Insurance's fraud detection team between 2015 and 2016. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their In this study, It is considered the performance of four different machine learning models for medical insurance premium prediction: linear regression, ridge regression, support vector machine (SVM), and random forest regression. This research trained and evaluated an artificial intelligence network-based regression-based model to predict health insurance premiums. Basic Knowledge of Power BI: Familiarity with Power BI's interface and features will be helpful. Additionally, the claims test set is scored using the claims frequency model since it relies on the ClaimNb prediction. Sep 18, 2023 · Accurate forecasting of insurance claims is of the utmost importance for insurance activity as the evolution of claims determines cash outflows and the pricing, and thus the profitability, of the underlying insurance coverage. Content This dataset comprises essential variables crucial for insurance analytics: Age: Policyholder's age. Accurate prediction gives a chance to reduce financial loss for the company. This paper explores how Generative Adversarial Networks (GANs) can help make insurance claims predictions more accurate by creating synthetic claim scenarios. We have used the USA's medical cost personal dataset from kaggle, having 1338 entries. Recently, many attempts have been made to solve this problem, as after Covid-19 pandemic, health insurance has become one of the most prominent areas of research. Apr 22, 2024 · This study aims to develop a deep learning model using sequential deep regression techniques for insurance claim prediction using historical data obtained from Kaggle with 1339 cases and eight variables. The target variable is a Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. This study introduces an alternative DL architecture, TabNet, suitable for insurance telematics datasets and claim prediction. Explore predictive analytics in insurance claim prediction for data scientists using business intelligence and DataCalculus insights. These are used as inputs when the insurance company drafts its business plan and determines its risk appetite, and the respective solvency capital required (by the In this Data set we are Predicting the Insurance Claim by each user, Machine Learning algorithms for Regression analysis are used and Data Visualization are also performed to support Analysis. The model assists insurance providers in identifying high-risk customers, enabling data-driven decisions for underwriting and risk management. The claims data includes a variety of features related to drivers, vehicles, and claims. Oct 24, 2023 · Machine learning-based insurance claims modelling: part 1 Insurance is at its heart the business of prediction. Insurance companies are extremely interested in the prediction of the future. The CRISP-DM (Cross-Industry Standard Introduction In this study, I deal with a dataset given from the Brazilian insurance company, Porto Seguro. The predictive modeling and forecasting of the premiums paid in insurance field is very essential to fix the claim amount for Explore and run machine learning code with Kaggle Notebooks | Using data from Health insurance data set Feb 7, 2025 · The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Abstract In India, the Health insurance sector has suffered heavily based on claims and also premiums from a commercial concerning the viability of the insurance companies, the long-term premiums intimation, and the non-cooperation of claim settlement in the industry. Jan 30, 2021 · claims, the Recurrent Neural Network (RNN) model shows better performance than other regression models [22]. Apr 12, 2022 · The insurance industry, with its large datasets, is a natural place to use big data solutions. Low code machine learning library, specified for insurance tasks: prepare data, build model, implement into production. MassMutual, a prominent insurance and financial services firm, has amassed a dataset of nearly one million applicants, covering a 15-year span and containing health, behavioral, and financial information. GANs . Porto Seguro has been applying Machine Learning for more than 20 years and intends to make car insurance more accessible to everyone. Prediction of insurance claims severity using the allstate claims dataset. Mar 10, 2012 · Data Analyst Medical Insurance Cost Prediction The primary objective of this project was to employ regression models to predict the cost of medical insurance and gain valuable insights into its influencing factors. Predict whether the policyholder will file a claim in the next 6 months or not. It's commonly used for predictive modeling and analysis The claim severity model requires first filtering the claims dataset to only include observations where claims exist (that is, ClaimNb > 0). In another study related to health insurance claim data, a machine learn ng predictive regression model LASSO was developed to formulate a population health management in Japan [23]. Class 0 indicates that the claim was not approved immediately (probably because it Feb 22, 2021 · The Dataset 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 This repository features code for the Allstate Claims Severity Kaggle competition, utilizing Python, primarily XGBoost, and LightGBM for predicting insurance claim losses. - sh Dec 8, 2023 · Modeling Car Insurance Claim Outcome using ML algorithms INTRODUCTION The insurance industry, particularly in the domain of car insurance, is a complex and dynamic landscape where companies strive The data consist of automobile insurance claims from the Allstate Insurance Company, and were posted for the Kaggle competition called the "Claim Prediction Challenge", which was run from July 13 to October 12 2011. Nov 29, 2020 · Analytics Vidhya Making an Insurance Claims Prediction model with CatBoost in R Harish Nagpal Follow 6 min read Jun 28, 2022 · Insurance businesses use ML to provide clients with accurate, quick, and efficient health insurance coverage. Jun 15, 2023 · Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. 7, Anaconda and jupyter. This will be a binary classification task and I will demonstrate few auto ML model using Dataiku DSS Platform like Logistic Regression, and Random Forest. Features in the dataset that are used for the prediction of The use of machine learning in life insurance claims prediction has significant potential benefits for insurers and policyholders, including streamlining the claims process, reducing fraud, and improving transparency. brkenq pezds fwsht gwmbilt udch iazvn ncdd qpepns htsvx djpcgb rypfwl tpmd zlh vqgev eoeqtug