Analysis of ‘US Health Insurance Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/teertha/ushealthinsurancedataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The venerable insurance industry is no stranger to data driven decision making. Yet in today's rapidly transforming digital landscape, Insurance is struggling to adapt and benefit from new technologies compared to other industries, even within the BFSI sphere (compared to the Banking sector for example.) Extremely complex underwriting rule-sets that are radically different in different product lines, many non-KYC environments with a lack of centralized customer information base, complex relationship with consumers in traditional risk underwriting where sometimes customer centricity runs reverse to business profit, inertia of regulatory compliance - are some of the unique challenges faced by Insurance Business.

Despite this, emergent technologies like AI and Block Chain have brought a radical change in Insurance, and Data Analytics sits at the core of this transformation. We can identify 4 key factors behind the emergence of Analytics as a crucial part of InsurTech:

• Big Data: The explosion of unstructured data in the form of images, videos, text, emails, social media
• AI: The recent advances in Machine Learning and Deep Learning that can enable businesses to gain insight, do predictive analytics and build cost and time - efficient innovative solutions
• Real time Processing: Ability of real time information processing through various data feeds (for ex. social media, news)
• Increased Computing Power: a complex ecosystem of new analytics vendors and solutions that enable carriers to combine data sources, external insights, and advanced modeling techniques in order to glean insights that were not possible before.

This dataset can be helpful in a simple yet illuminating study in understanding the risk underwriting in Health Insurance, the interplay of various attributes of the insured and see how they affect the insurance premium.

Content

This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. There are no missing or undefined values in the dataset.

Inspiration

This relatively simple dataset should be an excellent starting point for EDA, Statistical Analysis and Hypothesis testing and training Linear Regression models for predicting Insurance Premium Charges.

Proposed Tasks: - Exploratory Data Analytics - Statistical hypothesis testing - Statistical Modeling - Linear Regression

--- Original source retains full ownership of the source dataset ---

The dataset contains Year Wise Insurer Wise Health Insurance Business In Respect Of Health Products Offered By Life Insurers - New Business from Handbook on Indian Insurance Statistics

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in United States by age, education, race, gender, work experience and more.

This dataset is about books. It has 1 row and is filtered where the book is The cost of health insurance administration : an economic analysis. It features 7 columns including author, publication date, language, and book publisher.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

United States Health Insurance: Enrollment: Dental data was reported at 47.000 USD mn in 2023. This records an increase from the previous number of 46.000 USD mn for 2022. United States Health Insurance: Enrollment: Dental data is updated yearly, averaging 41.000 USD mn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 47.000 USD mn in 2023 and a record low of 28.000 USD mn in 2007. United States Health Insurance: Enrollment: Dental data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

Most health insurance in the USA is provided by employers until eligibility for public health insurance (Medicare) begins at age 65. Retiring before 65 exposes workers who lack retiree health insurance coverage to the risk of catastrophic medical expenditure. We solve and estimate a dynamic model of the employment behavior of older married couples that includes risky medical expenditure and health insurance. Parameter estimates imply that the risk-reducing feature of health insurance can account for about half of the observed association between retiree health insurance and employment for married men, but can account for only one tenth of the much larger observed association for married women. Policy simulations imply very small effects on employment of changing the age of eligibility for Medicare from 65 to 67.

This dataset is about books. It has 1 row and is filtered where the book is An examination of the potential costs of Universal Health Insurance in Ireland. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about books. It has 1 row and is filtered where the book is Size matters : the health insurance market for small firms. It features 7 columns including author, publication date, language, and book publisher.

This dataset shows the plan-level data on rating business rules, such as allowed relationships (e.g., spouse, dependents) and tobacco use by the Centers for Medicare & Medicaid Services (CMS).

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Marin County, California by age, education, race, gender, work experience and more.

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The table Business Rules is part of the dataset United States Health Insurance Marketplace, available at https://redivis.com/datasets/rwbg-a0a84qktj. It contains 21085 rows across 23 variables.

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Robeson County, North Carolina by age, education, race, gender, work experience and more.

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in Middletown, New York by age, education, race, gender, work experience and more.

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Melbourne, Florida by age, education, race, gender, work experience and more.

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Oneida County, New York by age, education, race, gender, work experience and more.

Worked full-time, year round in the past 12 months Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in Ontario, California by age, education, race, gender, work experience and more.

Did not work Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in New Jersey by age, education, race, gender, work experience and more.