보험 사기 탐지 시장 규모, 점유율, 성장 및 세계 업계 분석 : 유형 및 용도별, 지역별 인사이트 및 예측(2026-2034년)

Growth Factors of insurance fraud detection Market

The global insurance fraud detection market was valued at USD 6.46 billion in 2025 and is projected to grow significantly to USD 7.90 billion in 2026. The market is further expected to reach USD 46.61 billion by 2034, expanding at a remarkable CAGR of 24.90% during the forecast period (2026-2034).

North America dominated the global market with a 43.80% market share in 2025, driven by a mature insurance ecosystem and strong regulatory compliance requirements.

Insurance fraud detection solutions use advanced technologies such as artificial intelligence (AI), machine learning (ML), predictive analytics, big data, and cloud computing to detect suspicious claims, underwriting irregularities, identity fraud, and billing fraud. Rising digital transactions and increasing insurance penetration globally are accelerating demand for advanced fraud prevention systems.

Impact of Generative AI

Generative AI is transforming the insurance fraud detection market by enabling faster system integration and real-time analytics. AI-powered automation can generate ETL pipelines, API scaffolding, and data mappings, reducing deployment time. It enhances fraud detection accuracy by identifying anomalies and suspicious behavioral patterns in real time. This reduces investigation time, lowers operational costs, and improves detection rates, contributing to the rapid expansion of the market from USD 7.90 billion in 2026 to USD 46.61 billion by 2034.

Market Dynamics

Market Drivers

Rise in Organized Fraud Rings and Staged Accidents

The growing sophistication of organized fraud networks and staged accident schemes is a major growth driver. Traditional Special Investigation Units (SIUs) struggle to manage cross-carrier and cross-channel fraud activities. As financial losses rise, insurers are increasingly adopting AI-based analytics, machine learning algorithms, and graph-based intelligence systems to detect fraud early and minimize risk exposure.

Market Restraints

Data Silos and Inconsistent Data Quality

Fragmented data systems across policy, claims, and billing departments hinder effective fraud detection. Poor data integration and inconsistent datasets reduce predictive accuracy. Overcoming these challenges requires standardized data management frameworks and interoperable platforms that enable seamless information sharing.

Market Opportunities

Strategic Partnerships with Data Vendors

Collaborations with telematics providers, medical billing platforms, credit scoring firms, and geospatial analytics companies are creating new growth opportunities. These partnerships enhance fraud risk scoring, improve pre-payment verification processes, and reduce claim leakages. Mid-sized insurers are increasingly adopting advanced fraud detection tools through SaaS models, expanding market reach.

Market Trends

Shift Toward Real-Time ML & Graph-Based Analytics

The industry is transitioning from rule-based batch systems to real-time machine learning and graph analytics. These systems detect suspicious relationships and fraud networks instantly at the point of claim submission. Real-time fraud prevention improves customer experience by enabling faster legitimate claim approvals while reducing financial leakage.

Segmentation Analysis

By Deployment

The market is segmented into cloud and on-premise.

• The cloud segment is witnessing strong growth due to migration of core insurance workloads to SaaS platforms. Cloud solutions provide scalability, AI integration, and enterprise-grade security.
• Accelerated modernization is driving cloud adoption, especially among mid-sized carriers seeking cost-efficient innovation.

By Fraud Type

• Claims fraud dominates the market due to high claim volumes and rapid payout cycles.
• Identity fraud is expected to witness the highest growth rate, driven by increased digital onboarding and exposure to synthetic identity risks.

By Insurance Line

• Property & Casualty (P&C) insurance holds the largest share due to high claim volumes and complex fraud typologies such as staged accidents and inflated repairs.
• Health insurance is expected to grow at the fastest rate due to billing-related fraud schemes and expansion of digital healthcare services.

Regional Outlook

North America

North America recorded USD 2.83 billion in 2025 and is projected to reach USD 3.51 billion in 2026. The U.S. accounted for USD 2.18 billion in 2025 and is expected to reach USD 2.69 billion in 2026, driven by high fraud-related financial losses and rapid digitization.

Europe

Europe generated USD 1.47 billion in 2025, supported by strict regulations such as GDPR and AML directives. The U.K., Germany, and France are major contributors.

Asia Pacific

Asia Pacific is the fastest-growing region, with rapid digitization and regulatory expansion. India and China are emerging as key markets, contributing significantly by 2026.

South America & Middle East & Africa

South America reached USD 0.30 billion in 2025, while the Middle East & Africa accounted for USD 0.42 billion in 2025, driven by increasing insurance adoption and fraud awareness.

Competitive Landscape

Key players operating in the market include Verisk Analytics, LexisNexis Risk Solutions, DXC Technology Company, Shift Technology, IBM Corporation, SAS Institute, Experian, FICO, BAE Systems, and Feedzai.

Companies are focusing on AI innovation, cloud-based deployment, mergers & acquisitions, and strategic alliances to strengthen their market presence.

Recent developments in 2025 include new fraud detection alliances, AI-powered document verification platforms, and expanded reinsurance partnerships to enhance fraud loss protection capabilities.

Conclusion

The global insurance fraud detection market is experiencing rapid expansion, growing from USD 6.46 billion in 2025 to a projected USD 46.61 billion by 2034. Increasing organized fraud activities, regulatory pressure, and accelerated digital transformation are key growth drivers. While data integration challenges persist, advancements in AI, cloud computing, and real-time analytics are reshaping the fraud detection landscape. North America remains the leading region, while Asia Pacific is emerging as the fastest-growing market. Over the forecast period, technological innovation and strategic partnerships will play a critical role in strengthening fraud prevention frameworks across the global insurance industry.

Segmentation By Fraud Type, Deployment, Insurance Line and Region

ByFraud Type * Claims Fraud

• Identity Fraud
• Payment & Billing Fraud
• Application Fraud (fraud at policy issuance)
• Others

ByDeployment * Cloud

• On Premise

ByInsurance Line * Life Insurance

• Health Insurance
• Property & Casualty (P&C) Insurance
• Motor Insurance
• Others (Marine, Etc.)

By Region * North America (By Fraud Type, Deployment, Insurance Line and Country/Sub-region)

• U.S.
• Canada
• Mexico
• Europe (By Fraud Type, Deployment, Insurance Line and Country/Sub-region)
  • U.K.
  • Germany
  • France
  • Italy
  • Spain
  • Russia
  • Benelux
  • Nordics
  • Rest of Europe
• Asia Pacific (By Fraud Type, Deployment, Insurance Line and Country/Sub-region)
  • China
  • India
  • Japan
  • South Korea
  • ASEAN
  • Oceania
  • Rest of Asia Pacific
• South America (By Fraud Type, Deployment, Insurance Line and Country/Sub-region)
  • Argentina
  • Brazil
  • Rest of South America
• Middle East & Africa (By Fraud Type, Deployment, Insurance Line and Country/Sub-region)
  • Turkey
  • Israel
  • GCC
  • South Africa
  • North Africa
  • Rest of the Middle East & Africa

Table of Content

1. Introduction

• 1.1. Definition, By Segment
• 1.2. Research Methodology/Approach
• 1.3. Data Sources

2. Executive Summary

3. Market Dynamics

• 3.1. Macro and Micro Economic Indicators
• 3.2. Drivers, Restraints, Opportunities and Trends

4. Competition Landscape

• 4.1. Business Strategies Adopted by Key Players
• 4.2. Consolidated SWOT Analysis of Key Players
• 4.3. Global Insurance Fraud Detection Key Players (Top 3 - 5) Market Share/Ranking, 2025

5. Global Insurance Fraud Detection Market Size Estimates and Forecasts, By Segments, 2021-2034

• 5.1. Key Findings
• 5.2. By Fraud Type (USD Bn)
  • 5.2.1. Claims Fraud
  • 5.2.2. Identity Fraud
  • 5.2.3. Payment & Billing Fraud
  • 5.2.4. Application Fraud (fraud at policy issuance)
  • 5.2.5. Others
• 5.3. By Deployment (USD Bn)
  • 5.3.1. Cloud
  • 5.3.2. On Premise
• 5.4. By Insurance Line (USD Bn)
  • 5.4.1. Life Insurance
  • 5.4.2. Health Insurance
  • 5.4.3. Property & Casualty (P&C) Insurance
  • 5.4.4. Motor Insurance
  • 5.4.5. Others (Marine, Etc.)
• 5.5. By Region (USD Bn)
  • 5.5.1. North America
  • 5.5.2. Europe
  • 5.5.3. Asia Pacific
  • 5.5.4. Middle East & Africa
  • 5.5.5. South America

6. North America Insurance Fraud Detection Market Size Estimates and Forecasts, By Segments, 2021-2034

• 6.1. Key Findings
• 6.2. By Fraud Type (USD Bn)
  • 6.2.1. Claims Fraud
  • 6.2.2. Identity Fraud
  • 6.2.3. Payment & Billing Fraud
  • 6.2.4. Application Fraud (fraud at policy issuance)
  • 6.2.5. Others
• 6.3. By Deployment (USD Bn)
  • 6.3.1. Cloud
  • 6.3.2. On Premise
• 6.4. By Insurance Line (USD Bn)
  • 6.4.1. Life Insurance
  • 6.4.2. Health Insurance
  • 6.4.3. Property & Casualty (P&C) Insurance
  • 6.4.4. Motor Insurance
  • 6.4.5. Others (Marine, Etc.)
• 6.5. By Country (USD Bn)
  • 6.5.1. U.S.
  • 6.5.2. Canada
  • 6.5.3. Mexico

7. South America Insurance Fraud Detection Market Size Estimates and Forecasts, By Segments, 2021-2034

• 7.1. Key Findings
• 7.2. By Fraud Type (USD Bn)
  • 7.2.1. Claims Fraud
  • 7.2.2. Identity Fraud
  • 7.2.3. Payment & Billing Fraud
  • 7.2.4. Application Fraud (fraud at policy issuance)
  • 7.2.5. Others
• 7.3. By Deployment (USD Bn)
  • 7.3.1. Cloud
  • 7.3.2. On Premise
• 7.4. By Insurance Line (USD Bn)
  • 7.4.1. Life Insurance
  • 7.4.2. Health Insurance
  • 7.4.3. Property & Casualty (P&C) Insurance
  • 7.4.4. Motor Insurance
  • 7.4.5. Others (Marine, Etc.)
• 7.5. By Country (USD Bn)
  • 7.5.1. Brazil
  • 7.5.2. Argentina
  • 7.5.3. Rest of South America

8. Europe Insurance Fraud Detection Market Size Estimates and Forecasts, By Segments, 2021-2034

• 8.1. Key Findings
• 8.2. By Fraud Type (USD Bn)
  • 8.2.1. Claims Fraud
  • 8.2.2. Identity Fraud
  • 8.2.3. Payment & Billing Fraud
  • 8.2.4. Application Fraud (fraud at policy issuance)
  • 8.2.5. Others
• 8.3. By Deployment (USD Bn)
  • 8.3.1. Cloud
  • 8.3.2. On Premise
• 8.4. By Insurance Line (USD Bn)
  • 8.4.1. Life Insurance
  • 8.4.2. Health Insurance
  • 8.4.3. Property & Casualty (P&C) Insurance
  • 8.4.4. Motor Insurance
  • 8.4.5. Others (Marine, Etc.)
• 8.5. By Country (USD Bn)
  • 8.5.1. U.K.
  • 8.5.2. Germany
  • 8.5.3. France
  • 8.5.4. Italy
  • 8.5.5. Spain
  • 8.5.6. Russia
  • 8.5.7. Benelux
  • 8.5.8. Nordics
  • 8.5.9. Rest of Europe

9. Middle East & Africa Insurance Fraud Detection Market Size Estimates and Forecasts, By Segments, 2021-2034

• 9.1. Key Findings
• 9.2. By Fraud Type (USD Bn)
  • 9.2.1. Claims Fraud
  • 9.2.2. Identity Fraud
  • 9.2.3. Payment & Billing Fraud
  • 9.2.4. Application Fraud (fraud at policy issuance)
  • 9.2.5. Others
• 9.3. By Deployment (USD Bn)
  • 9.3.1. Cloud
  • 9.3.2. On Premise
• 9.4. By Insurance Line (USD Bn)
  • 9.4.1. Life Insurance
  • 9.4.2. Health Insurance
  • 9.4.3. Property & Casualty (P&C) Insurance
  • 9.4.4. Motor Insurance
  • 9.4.5. Others (Marine, Etc.)
• 9.5. By Country (USD Bn)
  • 9.5.1. Turkey
  • 9.5.2. Israel
  • 9.5.3. GCC
  • 9.5.4. South Africa
  • 9.5.5. North Africa
  • 9.5.6. Rest of MEA

10. Asia Pacific Insurance Fraud Detection Market Size Estimates and Forecasts, By Segments, 2021-2034

• 10.1. Key Findings
• 10.2. By Fraud Type (USD Bn)
  • 10.2.1. Claims Fraud
  • 10.2.2. Identity Fraud
  • 10.2.3. Payment & Billing Fraud
  • 10.2.4. Application Fraud (fraud at policy issuance)
  • 10.2.5. Others
• 10.3. By Deployment (USD Bn)
  • 10.3.1. Cloud
  • 10.3.2. On Premise
• 10.4. By Insurance Line (USD Bn)
  • 10.4.1. Life Insurance
  • 10.4.2. Health Insurance
  • 10.4.3. Property & Casualty (P&C) Insurance
  • 10.4.4. Motor Insurance
  • 10.4.5. Others (Marine, Etc.)
• 10.5. By Country (USD Bn)
  • 10.5.1. China
  • 10.5.2. India
  • 10.5.3. Japan
  • 10.5.4. South Korea
  • 10.5.5. ASEAN
  • 10.5.6. Oceania
  • 10.5.7. Rest of Asia Pacific

11. Company Profiles for Top 10 Players (Based on data availability in public domain and/or on paid databases)

• 11.1. Verisk Analytics
  • 11.1.1. Overview
    • 11.1.1.1. Key Management
    • 11.1.1.2. Headquarters
    • 11.1.1.3. Offerings/Business Segments
  • 11.1.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.1.2.1. Employee Size
    • 11.1.2.2. Past and Current Revenue
    • 11.1.2.3. Geographical Share
    • 11.1.2.4. Business Segment Share
    • 11.1.2.5. Recent Developments
• 11.2. LexisNexis Risk Solution
  • 11.2.1. Overview
    • 11.2.1.1. Key Management
    • 11.2.1.2. Headquarters
    • 11.2.1.3. Offerings/Business Segments
  • 11.2.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.2.2.1. Employee Size
    • 11.2.2.2. Past and Current Revenue
    • 11.2.2.3. Geographical Share
    • 11.2.2.4. Business Segment Share
    • 11.2.2.5. Recent Developments
• 11.3. DXC Technology Company
  • 11.3.1. Overview
    • 11.3.1.1. Key Management
    • 11.3.1.2. Headquarters
    • 11.3.1.3. Offerings/Business Segments
  • 11.3.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.3.2.1. Employee Size
    • 11.3.2.2. Past and Current Revenue
    • 11.3.2.3. Geographical Share
    • 11.3.2.4. Business Segment Share
    • 11.3.2.5. Recent Developments
• 11.4. Shift Technology
  • 11.4.1. Overview
    • 11.4.1.1. Key Management
    • 11.4.1.2. Headquarters
    • 11.4.1.3. Offerings/Business Segments
  • 11.4.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.4.2.1. Employee Size
    • 11.4.2.2. Past and Current Revenue
    • 11.4.2.3. Geographical Share
    • 11.4.2.4. Business Segment Share
    • 11.4.2.5. Recent Developments
• 11.5. IBM Corporation
  • 11.5.1. Overview
    • 11.5.1.1. Key Management
    • 11.5.1.2. Headquarters
    • 11.5.1.3. Offerings/Business Segments
  • 11.5.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.5.2.1. Employee Size
    • 11.5.2.2. Past and Current Revenue
    • 11.5.2.3. Geographical Share
    • 11.5.2.4. Business Segment Share
    • 11.5.2.5. Recent Developments
• 11.6. SAS Institute
  • 11.6.1. Overview
    • 11.6.1.1. Key Management
    • 11.6.1.2. Headquarters
    • 11.6.1.3. Offerings/Business Segments
  • 11.6.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.6.2.1. Employee Size
    • 11.6.2.2. Past and Current Revenue
    • 11.6.2.3. Geographical Share
    • 11.6.2.4. Business Segment Share
    • 11.6.2.5. Recent Developments
• 11.7. OpenPayd
  • 11.7.1. Overview
    • 11.7.1.1. Key Management
    • 11.7.1.2. Headquarters
    • 11.7.1.3. Offerings/Business Segments
  • 11.7.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.7.2.1. Employee Size
    • 11.7.2.2. Past and Current Revenue
    • 11.7.2.3. Geographical Share
    • 11.7.2.4. Business Segment Share
    • 11.7.2.5. Recent Developments
• 11.8. Experian
  • 11.8.1. Overview
    • 11.8.1.1. Key Management
    • 11.8.1.2. Headquarters
    • 11.8.1.3. Offerings/Business Segments
  • 11.8.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.8.2.1. Employee Size
    • 11.8.2.2. Past and Current Revenue
    • 11.8.2.3. Geographical Share
    • 11.8.2.4. Business Segment Share
    • 11.8.2.5. Recent Developments
• 11.9. FICO
  • 11.9.1. Overview
    • 11.9.1.1. Key Management
    • 11.9.1.2. Headquarters
    • 11.9.1.3. Offerings/Business Segments
  • 11.9.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.9.2.1. Employee Size
    • 11.9.2.2. Past and Current Revenue
    • 11.9.2.3. Geographical Share
    • 11.9.2.4. Business Segment Share
    • 11.9.2.5. Recent Developments
• 11.10. BAE System
  • 11.10.1. Overview
    • 11.10.1.1. Key Management
    • 11.10.1.2. Headquarters
    • 11.10.1.3. Offerings/Business Segments
  • 11.10.2. Key Details (Key details are consolidated data and not product/service specific)
    • 11.10.2.1. Employee Size
    • 11.10.2.2. Past and Current Revenue
    • 11.10.2.3. Geographical Share
    • 11.10.2.4. Business Segment Share
    • 11.10.2.5. Recent Developments

12. Key Takeaways