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.

The data set contains the insurance company wise number of Life insurance claims settled. The information is as per the respective public disclosures of the insurance companies made on IRDAI portal.

Predict earnings surprises, measure growth across procedures and infusion therapeutics, and track macro utilization trends derived from domestic medical claims. Leo medical claims data is sourced from the largest US healthcare claims clearinghouse.

Graph and download economic data for Initial Claims (ICNSA) from 1967-01-07 to 2025-05-31 about initial claims and USA.

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 Workers’ Compensation Board (WCB) administers and regulates workers’ compensation benefits, disability benefits, volunteer firefighters’ benefits, volunteer ambulance workers’ benefits, and volunteer civil defense workers’ benefits. The WCB processes and adjudicates claims for benefits; ensures employer compliance with the requirement to maintain appropriate insurance coverage; and regulates the various system stakeholders, including self-insured employers, medical providers, third party administrators, insurance carriers and legal representatives. Claim assembly occurs when the WCB learns of a workplace injury and assigns the claim a WCB claim number. The WCB “assembles” a claim in which an injured worker has lost more than one week of work, has a serious injury that may result in a permanent disability, is disputed by the carrier or employer, or receives a claim form from the injured worker (Form C-3). A reopened claim is one that has been reactivated to resolve new issues following a finding that no further action was necessary

Analyze complete patient journeys across both medical and pharmacy claims and accurately track metrics like patient persistence, therapy switches, and concomitant therapies. Medical claims data is sourced from a large health service company with visibility into unblinded provider identities and strong longitudinal integrity allowing for accurate patient journey analytics.

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.

Insurance claims processing software in the US has experienced significant transformative trends over the past decade, marked by a CAGR of 4.3% to reach $12.7 in 2024. The pandemic fundamentally altered focus areas within the industry - pivoting resources from auto to health and life insurance while expediting the need for integrated claims processing systems. Software developers swiftly adapted their solutions to manage escalating volumes of health-related claims and ensure seamless integration of underwriting accuracy and real-time claims data, thus bolstering decision-making efficiency and customer satisfaction. Stakeholder trust has married intricacies in insurance claims processing software with broader technological advances, particularly predictive analytics and machine learning. Predictive analytics has allowed insurers to adjust policies and premiums more precisely based on current data, magnifying interface capabilities between underwriting and claims management; consequently, revenue has expanded by 6.2% in 2024. However, achieving this advancement necessitates a delicate balance of privacy and data protection as regulatory frameworks intensify to mitigate potential biases and uphold transparency within AI-driven claims adjudications. Looking forward, significant trends are set to dictate the insurance software's developmental trajectory as it expands at a CAGR of 1.9% to $13.9 billion in 2029. Natural disasters necessitate constantly upgrading and refining claims processing algorithms to efficiently evaluate and manage disaster-related claims. An ongoing pivot towards cloud computing models anticipates more agile, scalable solutions for remote accessibility and security. The adoption of non-submitted public data to corroborate insurance claims is poised to both accelerate processing speeds and introduce additional layers of security and privacy considerations. For this industry, success will hinge on vigorously updating legacy systems, forging innovative solutions, and responding briskly to technological advancements while navigating an ever-evolving regulatory landscape. Companies must engage skilled IT professionals proficient in software and insurance terminologies to maintain a competitive edge. As the industry contends with record software developer wages and significant investment demands, operational agility, robust customer service and effective leveraging of advanced analytics will remain cornerstones for sustained profitability and market relevance in the future.

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.

Project Objectives 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 behaviour of potentially fraud providers. further, we will study fraudulent patterns in the provider's claims to understand the future behaviour of providers.

Introduction to 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,regioregion they belong to etc.

Initial Jobless Claims in the United States increased to 247 thousand in the week ending May 31 of 2025 from 239 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

2016-2019. This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the Medicaid Analytic eXtract (MAX) data. Medicaid MAX are a set of de-identified person-level data files with information on Medicaid eligibility, service utilization, diagnoses, and payments. The MAX data contain a convenience sample of claims processed by Medicaid and Children’s Health Insurance Program (CHIP) fee for service and managed care plans. Not all states are included in MAX in all years, and as of November 2019, 2014 data is the latest available. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS Medicare analyses can be found on the VEHSS Medicaid MAX webpage (cdc.gov/visionhealth/vehss/data/claims/medicaid.html). Information on available Medicare claims data can be found on the ResDac website (www.resdac.org). The VEHSS Medicaid MAX dataset was last updated May 2023.

Weekly unemployment insurance claims counts and rates (as a share of the 2019 labor force) for Connecticut from the U.S. Department of Labor, compiled by Opportunity Insights. Breakdowns by claim type: Initial Claims – Regular Claims – PUA Claims – Combined Claims Continued Claims – Regular Claims – PUA Claims – PEUC Claims – Combined Claims More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

Graph and download economic data for Continued Claims (Insured Unemployment) in the District of Columbia (DCCCLAIMS) from 1986-01-04 to 2025-05-24 about continued claims, DC, insurance, unemployment, and USA.

Losses caused by lightning in the United States were the cause behind a total of 70,787 insurance claims paid by homeowner insurance companies in 2023. In 2008, lightning caused around 246,000 homeowner insurance claims in the same country.

Track specialty drug utilization, analyze patient journeys, and predict earnings surprises based on domestic pharmacy claims capturing ~ 90 million patients. Pharmacy claims data is sourced from a large health services company with visibility into commonly blocked specialty pharmacy drugs and strong longitudinal integrity allowing for accurate patient journey analytics.

Global Claims Processing Software market size is expected to reach $66.23 billion by 2029 at 9.7%, claims processing software market driven by the imperative to lower compliance risk exposure

Claims Adjusting industry companies handle property claims involving damage to structures and liability claims involving personal injuries or third-person property damage. Insurance carriers and third-party claim-adjusting establishments have increasingly relied on these services to reduce operating costs and improve efficiency. Success in the industry is contingent on various factors, including professional experience, positive track record, cost-effectiveness and compliance. Since claims adjusters are an ancillary service to insurance providers, industry trends align with the broader finance and insurance sector. Overall, industry-wide revenue has been growing at a CAGR of 1.9% to $11.7 billion over the past five years, including an expected increase of 1.0% in 2024 alone. Strong industry revenue growth was limited somewhat by poor economic conditions in 2020 due to the pandemic. Also, an increase in individuals working from home contributed to a drop in vehicle traffic, resulting in a decline in automobile insurance claims in 2020. However, following the pandemic and stay-at-home restrictions, vehicle traffic increased significantly as individuals returned to the office, increasing demand for automobile insurance claims. Also, the number of motor vehicle registrations climbed, which boosted demand for automobile insurance. The rise in the homeownership rate helped boost demand for home insurance. The jump in devastating natural disasters increased demand for property claims and drove revenue growth. Furthermore, rising per capita disposable income has enabled consumers to increase their insurance coverage, increasing shares and growing claims adjustments. Industry revenue is forecast to grow at a CAGR of 1.1% to $12.4 billion over the five years to 2029. Rising vehicle traffic and the frequency and severity of natural disasters will bolster demand for claims-adjusting services. Moreover, as the broader finance and insurance sector expands, relevant companies will continuously outsource claims adjusting services, benefiting industry demand. During the outlook period, declines in the homeownership rate and in the number of vehicle accidents will reduce demand for insurance, contributing to limiting revenue growth. In addition, the Fed is anticipated to continue cutting interest rates as inflationary pressures ease, which will give way to investment in new businesses.