Property Insurance Complaints Statistics (Insurance Industry Development Center)

Dataset

This dataset was created by Bunty Shah

Contents

Losses caused by lightning in the United States were the cause behind a total of ****** insurance claims paid by homeowner insurance companies in 2023. In 2008, lightning caused around ******* homeowner insurance claims in the same country.

Between 2016 and 2020, registration and/or eligibility was the main reason for **** percent of health insurance claims being denied in the United States. Furthermore, missing or invalid claim data caused over ** percent of health insurance claims to be denied in this time period. This statistic illustrates the leading reasons for denials of healthcare claims in the United States (U.S.) in 2020.

The recent two years' statistics of property and casualty insurance claims rates - (annual) - by company (insurance development center)

Reinsurance: Premium Adequacy to Claim Paid Ratio data was reported at 0.000 % mn in Feb 2025. This records a decrease from the previous number of 0.000 % mn for Jan 2025. Reinsurance: Premium Adequacy to Claim Paid Ratio data is updated monthly, averaging 0.000 % mn from Jan 2016 (Median) to Feb 2025, with 110 observations. The data reached an all-time high of 0.001 % mn in Jan 2024 and a record low of 0.000 % mn in Dec 2020. Reinsurance: Premium Adequacy to Claim Paid Ratio data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA006: Insurance Statistics: Claim Ratio.

Oregon workers' compensation claims counts. Where available, the data is provided since 1968, the year Oregon's modern workers' compensation system began. The data is presented in the Department of Consumer and Business Services report at https://www.oregon.gov/dcbs/reports/compensation/Pages/index.aspx. The attached pdf provides definitions of the data.

The dataset includes fiscal year data for initial claims for SSA disability benefits that were referred to a state agency for a disability determination. Specific data elements for each year and state include receipts, determinations, eligible population, and favorable determination rates.

Insurance: Claim Incurred data was reported at 6,589.776 BRL mn in Feb 2025. This records a decrease from the previous number of 6,851.124 BRL mn for Jan 2025. Insurance: Claim Incurred data is updated monthly, averaging 4,074.157 BRL mn from Dec 2013 (Median) to Feb 2025, with 135 observations. The data reached an all-time high of 8,320.939 BRL mn in May 2024 and a record low of 2,525.717 BRL mn in Jun 2014. Insurance: Claim Incurred data remains active status in CEIC and is reported by Superintendence of Private Insurance. The data is categorized under Global Database’s Brazil – Table BR.RG002: Insurance: Claims. [COVID-19-IMPACT]

The number of motor insurance claims submitted in France dipped in 2020, but had recovered to pre-pandemic levels by 2022. In 2023, French motor insurance companies received *** million claims, which was a slight decrease from the nearly **** million claims submitted the previous year.

Problem Statement

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An insurance company faced significant inefficiencies in its claims processing operations. The manual review and assessment of claims were time-consuming, prone to errors, and resulted in delays that frustrated customers. The company needed a solution to streamline claims processing, reduce operational costs, and improve customer satisfaction.

Challenge

Automating insurance claims processing involved addressing several challenges:

Handling diverse claim types, including structured and unstructured data such as invoices, photographs, and customer narratives.

Ensuring accurate claims assessment while detecting potential fraud.

Integrating automation with existing systems without disrupting ongoing operations.

Solution Provided

An AI-powered claims processing system was developed using machine learning and workflow automation technologies. The solution was designed to:

Extract and validate data from claim submissions automatically.

Assess claims using predictive models to estimate coverage and liability.

Flag potential fraudulent claims for further investigation.

Development Steps

Data Collection

Collected historical claims data, including structured data from forms and unstructured data such as photos and handwritten notes, to train machine learning models.

Preprocessing

Standardized and cleaned data, ensuring compatibility across various sources. Applied optical character recognition (OCR) for extracting data from scanned documents.

Model Development

Developed machine learning models to evaluate claims based on historical trends and patterns. Built fraud detection algorithms to identify anomalies in claims data.

Validation

Tested the system with live claims data to ensure accuracy in assessment, fraud detection, and operational efficiency.

Deployment

Implemented the solution across the company’s claims processing system, enabling seamless operation and real-time processing.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and workflows based on new data and user feedback.

Results

Accelerated Claims Processing Time

The automation system reduced claims processing time by 60%, enabling quicker payouts and enhancing customer satisfaction.

Reduced Operational Costs

Automating routine tasks lowered operational costs by minimizing manual labor and administrative overhead.

Improved Customer Satisfaction

Faster and more accurate claims processing improved customer experience and strengthened trust in the company’s services.

Enhanced Fraud Detection

The system’s predictive algorithms flagged suspicious claims effectively, reducing the risk of fraudulent payouts.

Scalable and Adaptive Solution

The solution scaled seamlessly to handle increased claim volumes, ensuring consistent performance during peak periods.

This data set contains DOT employee workers compensation claim data for current and past DOT employees. Types of data include claim data consisting of PII data (SSN, Name, Date of Birth, Home Address, Financial Institution, medical, etc.) and claim data from the Department of Labor

In the third quarter of 2020, approximately ** percent of health insurance claims were rejected in the United States, the highest rate in the provided time interval. This statistic illustrate the national denial rate for health insurance claims in the United States from 2016 to Q3 2020.

The Medicaid Drug Claims Statistics CD is a useful tool that conveniently breaks up Medicaid claim counts and separates them by quarter and includes an annual count.

This dataset reports total weekly unemployment insurance initial claims and continued weeks claimed statewide in Iowa by week. Data for the most current week is preliminary and will be revised the following week. Initial claims data for states are combined and published weekly by the U.S. Department of Labor, Employment and Training Administration. This national data is widely reported as an economic indicator. This data is based on the ETA-539 report. This dataset is based on administrative data. Claims activity represents the week the claims were processed. It may not always represent the week unemployment occurred.

Data set listing the individual claims filed against the City of New York and the individual claims settled by the City of New York during the prior fiscal year.

1) Data Introduction • The Insurance Claim Dataset is a tabular dataset collected to predict whether an insurance claim will be made (yes/no) based on information such as the policyholder’s age, gender, BMI, average daily steps, number of children, smoking status, residential region, and medical charges billed by health insurance.

2) Data Utilization (1) Characteristics of the Insurance Claim Dataset: • The dataset integrates various factors such as health status, lifestyle habits, and demographic characteristics, making it suitable for practical use in insurance risk prediction and customer segmentation.

(2) Applications of the Insurance Claim Dataset: • Development of Insurance Claim Prediction Models: The dataset can be used to develop machine learning models that classify whether an insurance claim will be filed based on multiple input features. • Insurance Product Development and Risk Assessment: By analyzing the probability of claims for different customer profiles, the dataset can be used for product design, risk management, and premium pricing in practical policy planning.

Between the first half of 2021 and the end of 2023, the severity of claims for cyber insurance globally decreased overall, despite fluctuations. In the second half of 2023, the average claim amount for cyber insurance reached 86,592 U.S. dollars, a decrease of around 25,000 U.S. dollars as compared to the first half of 2021.

Reinsurance: Cession Ratio data was reported at 0.000 % mn in Feb 2025. This records a decrease from the previous number of 0.000 % mn for Jan 2025. Reinsurance: Cession Ratio data is updated monthly, averaging 0.000 % mn from Jan 2016 (Median) to Feb 2025, with 110 observations. The data reached an all-time high of 0.000 % mn in Jan 2024 and a record low of 0.000 % mn in Jan 2016. Reinsurance: Cession Ratio data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA006: Insurance Statistics: Claim Ratio.

Dataset

This dataset was created by Amit Bakde

Contents