AI-Powered Fraud Detection Market Forecasts to 2032 – Global Analysis By Component (Software and Services), Fraud Type, Deployment Model, Organization Size, Technology, End User and By Geography
Description
According to Stratistics MRC, the Global AI-Powered Fraud Detection Market is accounted for $14.2 billion in 2025 and is expected to reach $48.2 billion by 2032 growing at a CAGR of 19% during the forecast period. AI-Powered Fraud Detection is a technology-driven approach that uses artificial intelligence, machine learning, and advanced analytics to identify, prevent, and respond to fraudulent activities in real time. By analyzing large volumes of structured and unstructured data, AI systems can detect unusual patterns, anomalies, and suspicious behaviors that may indicate fraud. These systems continuously learn and adapt from new data, improving accuracy over time. AI-Powered Fraud Detection is widely applied in banking, e-commerce, insurance, and cybersecurity to safeguard transactions, reduce financial losses, and enhance trust. It enables faster, more efficient, and proactive fraud management compared to traditional methods.
Market Dynamics:
Driver:
Increasing cybercrime across financial sectors
Financial institutions require advanced systems to safeguard transactions and customer identities. AI-driven platforms are accelerating fraud detection by analyzing massive datasets in real time. Vendors are boosting adoption by embedding machine learning algorithms that adapt to evolving threats. Rising demand for secure financial ecosystems is fostering deployment across banking, insurance, and fintech. Enterprises are propelling investments in AI-powered fraud detection to strengthen compliance and operational trust. Growing cybercrime across financial sectors is positioning AI-driven fraud detection as a critical pillar of digital security.
Restraint:
Limited skilled AI security professionals
Organizations struggle to recruit talent capable of managing complex AI-driven platforms. Smaller firms are constrained by workforce gaps compared to incumbents with larger resources. Rising complexity of advanced analytics further hampers deployment initiatives. Vendors are fostering simplified interfaces and automation to reduce dependency on specialized skills. Persistent talent shortages limit scalability and degrade modernization timelines. Workforce constraints are reshaping adoption strategies and making skill development a decisive factor for success.
Opportunity:
Integration with cloud and blockchain technologies
Enterprises require secure frameworks to protect distributed data and digital transactions. Cloud-native platforms are boosting agility by enabling scalable fraud detection across hybrid environments. Vendors are propelling innovation by embedding blockchain-based transparency and immutable records into fraud prevention systems. Rising investment in digital transformation is fostering demand across BFSI and telecom ecosystems. Cloud and blockchain integration is accelerating fraud detection into a proactive enabler of secure connectivity. Growth in these technologies is positioning AI-powered fraud detection as a driver of trust in digital economies.
Threat:
Rapidly evolving sophisticated cyber attacks
Organizations face rising risks from advanced identity theft and credential-based intrusions. Smaller providers are constrained by limited resources to counter sophisticated attack vectors. Regulatory frameworks add complexity and hinder deployment strategies. Vendors are embedding encryption, behavioral analytics, and compliance features to mitigate risks. Growing sophistication of cyberattacks is degrading trust and reshaping priorities toward resilience. Advanced fraud tactics are redefining AI-powered detection as a frontline defense against evolving digital threats.
Covid-19 Impact:
The Covid-19 pandemic boosted demand for AI-powered fraud detection as digital transactions surged. On one hand, disruptions in workforce and supply chains hindered deployment projects. On the other hand, rising demand for secure remote financial services accelerated adoption of AI-driven platforms. Enterprises increasingly relied on real-time monitoring and adaptive analytics to sustain operations during volatile conditions. Vendors embedded advanced automation and compliance features to foster resilience. Covid-19 underscored AI-powered fraud detection as a vital enabler of trust and continuity in financial ecosystems.
The banking, financial services, and insurance (BFSI) segment is expected to be the largest during the forecast period
The banking, financial services, and insurance (BFSI) segment is expected to account for the largest market share during the forecast perio , driven by demand for scalable fraud detection frameworks. Enterprises are embedding AI-powered platforms into workflows to accelerate compliance and strengthen transaction security. Vendors are developing solutions that integrate automation, analytics, and identity verification features. Rising demand for secure digital-first operations is boosting adoption in this segment. BFSI institutions view fraud detection as critical for sustaining consumer trust and operational integrity. AI-powered systems are fostering fraud detection as the backbone of financial resilience.
The identity theft and account takeover segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the identity theft and account takeover segment is predicted to witness the highest growth rate, supported by rising demand for secure identity management. Financial institutions increasingly require AI-driven systems to protect customer accounts and digital identities. Vendors are embedding adaptive authentication and behavioral analytics to accelerate responsiveness. SMEs and large institutions benefit from scalable solutions tailored to diverse fraud scenarios. Rising investment in secure transaction frameworks is propelling demand in this segment. Identity theft prevention is fostering fraud detection as a catalyst for consumer protection.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share, supported by mature financial infrastructure and strong enterprise adoption of fraud detection frameworks. Enterprises in the United States and Canada are accelerating investments in AI-powered platforms. The presence of major technology providers further boosts regional dominance. Rising demand for compliance with data privacy regulations is propelling adoption across industries. Vendors are embedding advanced automation and analytics to foster differentiation in competitive markets. North America’s leadership is defined by its ability to merge innovation with regulatory discipline in fraud detection.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid digitalization, expanding mobile penetration, and government-led financial inclusion initiatives. Countries such as China, India, and Southeast Asia are accelerating investments in AI-powered fraud detection to support enterprise growth. Local startups are deploying cost-effective solutions tailored to diverse consumer bases. Enterprises are adopting AI-driven and cloud-native platforms to boost scalability and meet compliance expectations. Government programs promoting digital transformation are fostering adoption. Asia Pacific’s growth is being propelled by evolving fraud risks making it the most adaptive hub for fraud detection innovation.
Key players in the market
Some of the key players in AI-Powered Fraud Detection Market include IBM Corporation, SAS Institute Inc., FICO (Fair Isaac Corporation), BAE Systems plc, ACI Worldwide, Inc., NICE Actimize, Experian plc, LexisNexis Risk Solutions, Kount, Inc., Featurespace Ltd., Feedzai, Inc., Riskified Ltd., Darktrace Holdings Ltd., Mastercard Incorporated and Visa Inc.
Key Developments:
In April 2025, SAS announced a strategic collaboration with Microsoft to integrate its SAS® Viya® analytics platform with Microsoft Azure AI and cloud services, enhancing scalable AI-powered fraud detection solutions for joint financial services clients. This partnership specifically combined SAS's fraud analytics with Azure's AI capabilities to improve real-time transaction monitoring and model deployment.
In February 2025, IBM and HSBC deepened their strategic collaboration, focusing on leveraging IBM's AI and watsonx capabilities to enhance HSBC's financial crime detection and compliance frameworks. This multi-year agreement aimed to transform HSBC's transaction monitoring systems using generative AI to improve accuracy and reduce false positives.
Components Covered:
• Software
• Services
Fraud Types Covered:
• Payment Fraud
• Identity Theft and Account Takeover
• Insurance Fraud
• Loan and Credit Fraud
• E-Commerce and Retail Fraud
• Other Fraud Types
Deployment Models Covered:
• On-premise
• Cloud
Organization Sizes Covered:
• Small and Medium Enterprises (SMEs)
• Large Enterprises
Technologies Covered:
• Machine Learning and Deep Learning
• Natural Language Processing
• Behavioral Analytics
• Predictive Analytics
• Other Technologies
End Users Covered:
• Banking, Financial Services, and Insurance (BFSI)
• Healthcare and Life Sciences
• IT and Telecommunications
• Government and Public Sector
• Energy and Utilities
• Other End Users
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Market Dynamics:
Driver:
Increasing cybercrime across financial sectors
Financial institutions require advanced systems to safeguard transactions and customer identities. AI-driven platforms are accelerating fraud detection by analyzing massive datasets in real time. Vendors are boosting adoption by embedding machine learning algorithms that adapt to evolving threats. Rising demand for secure financial ecosystems is fostering deployment across banking, insurance, and fintech. Enterprises are propelling investments in AI-powered fraud detection to strengthen compliance and operational trust. Growing cybercrime across financial sectors is positioning AI-driven fraud detection as a critical pillar of digital security.
Restraint:
Limited skilled AI security professionals
Organizations struggle to recruit talent capable of managing complex AI-driven platforms. Smaller firms are constrained by workforce gaps compared to incumbents with larger resources. Rising complexity of advanced analytics further hampers deployment initiatives. Vendors are fostering simplified interfaces and automation to reduce dependency on specialized skills. Persistent talent shortages limit scalability and degrade modernization timelines. Workforce constraints are reshaping adoption strategies and making skill development a decisive factor for success.
Opportunity:
Integration with cloud and blockchain technologies
Enterprises require secure frameworks to protect distributed data and digital transactions. Cloud-native platforms are boosting agility by enabling scalable fraud detection across hybrid environments. Vendors are propelling innovation by embedding blockchain-based transparency and immutable records into fraud prevention systems. Rising investment in digital transformation is fostering demand across BFSI and telecom ecosystems. Cloud and blockchain integration is accelerating fraud detection into a proactive enabler of secure connectivity. Growth in these technologies is positioning AI-powered fraud detection as a driver of trust in digital economies.
Threat:
Rapidly evolving sophisticated cyber attacks
Organizations face rising risks from advanced identity theft and credential-based intrusions. Smaller providers are constrained by limited resources to counter sophisticated attack vectors. Regulatory frameworks add complexity and hinder deployment strategies. Vendors are embedding encryption, behavioral analytics, and compliance features to mitigate risks. Growing sophistication of cyberattacks is degrading trust and reshaping priorities toward resilience. Advanced fraud tactics are redefining AI-powered detection as a frontline defense against evolving digital threats.
Covid-19 Impact:
The Covid-19 pandemic boosted demand for AI-powered fraud detection as digital transactions surged. On one hand, disruptions in workforce and supply chains hindered deployment projects. On the other hand, rising demand for secure remote financial services accelerated adoption of AI-driven platforms. Enterprises increasingly relied on real-time monitoring and adaptive analytics to sustain operations during volatile conditions. Vendors embedded advanced automation and compliance features to foster resilience. Covid-19 underscored AI-powered fraud detection as a vital enabler of trust and continuity in financial ecosystems.
The banking, financial services, and insurance (BFSI) segment is expected to be the largest during the forecast period
The banking, financial services, and insurance (BFSI) segment is expected to account for the largest market share during the forecast perio , driven by demand for scalable fraud detection frameworks. Enterprises are embedding AI-powered platforms into workflows to accelerate compliance and strengthen transaction security. Vendors are developing solutions that integrate automation, analytics, and identity verification features. Rising demand for secure digital-first operations is boosting adoption in this segment. BFSI institutions view fraud detection as critical for sustaining consumer trust and operational integrity. AI-powered systems are fostering fraud detection as the backbone of financial resilience.
The identity theft and account takeover segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the identity theft and account takeover segment is predicted to witness the highest growth rate, supported by rising demand for secure identity management. Financial institutions increasingly require AI-driven systems to protect customer accounts and digital identities. Vendors are embedding adaptive authentication and behavioral analytics to accelerate responsiveness. SMEs and large institutions benefit from scalable solutions tailored to diverse fraud scenarios. Rising investment in secure transaction frameworks is propelling demand in this segment. Identity theft prevention is fostering fraud detection as a catalyst for consumer protection.
Region with largest share:
During the forecast period, the North America region is expected to hold the largest market share, supported by mature financial infrastructure and strong enterprise adoption of fraud detection frameworks. Enterprises in the United States and Canada are accelerating investments in AI-powered platforms. The presence of major technology providers further boosts regional dominance. Rising demand for compliance with data privacy regulations is propelling adoption across industries. Vendors are embedding advanced automation and analytics to foster differentiation in competitive markets. North America’s leadership is defined by its ability to merge innovation with regulatory discipline in fraud detection.
Region with highest CAGR:
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid digitalization, expanding mobile penetration, and government-led financial inclusion initiatives. Countries such as China, India, and Southeast Asia are accelerating investments in AI-powered fraud detection to support enterprise growth. Local startups are deploying cost-effective solutions tailored to diverse consumer bases. Enterprises are adopting AI-driven and cloud-native platforms to boost scalability and meet compliance expectations. Government programs promoting digital transformation are fostering adoption. Asia Pacific’s growth is being propelled by evolving fraud risks making it the most adaptive hub for fraud detection innovation.
Key players in the market
Some of the key players in AI-Powered Fraud Detection Market include IBM Corporation, SAS Institute Inc., FICO (Fair Isaac Corporation), BAE Systems plc, ACI Worldwide, Inc., NICE Actimize, Experian plc, LexisNexis Risk Solutions, Kount, Inc., Featurespace Ltd., Feedzai, Inc., Riskified Ltd., Darktrace Holdings Ltd., Mastercard Incorporated and Visa Inc.
Key Developments:
In April 2025, SAS announced a strategic collaboration with Microsoft to integrate its SAS® Viya® analytics platform with Microsoft Azure AI and cloud services, enhancing scalable AI-powered fraud detection solutions for joint financial services clients. This partnership specifically combined SAS's fraud analytics with Azure's AI capabilities to improve real-time transaction monitoring and model deployment.
In February 2025, IBM and HSBC deepened their strategic collaboration, focusing on leveraging IBM's AI and watsonx capabilities to enhance HSBC's financial crime detection and compliance frameworks. This multi-year agreement aimed to transform HSBC's transaction monitoring systems using generative AI to improve accuracy and reduce false positives.
Components Covered:
• Software
• Services
Fraud Types Covered:
• Payment Fraud
• Identity Theft and Account Takeover
• Insurance Fraud
• Loan and Credit Fraud
• E-Commerce and Retail Fraud
• Other Fraud Types
Deployment Models Covered:
• On-premise
• Cloud
Organization Sizes Covered:
• Small and Medium Enterprises (SMEs)
• Large Enterprises
Technologies Covered:
• Machine Learning and Deep Learning
• Natural Language Processing
• Behavioral Analytics
• Predictive Analytics
• Other Technologies
End Users Covered:
• Banking, Financial Services, and Insurance (BFSI)
• Healthcare and Life Sciences
• IT and Telecommunications
• Government and Public Sector
• Energy and Utilities
• Other End Users
Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Table of Contents
200 Pages
- 1 Executive Summary
- 2 Preface
- 2.1 Abstract
- 2.2 Stake Holders
- 2.3 Research Scope
- 2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
- 2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions
- 3 Market Trend Analysis
- 3.1 Introduction
- 3.2 Drivers
- 3.3 Restraints
- 3.4 Opportunities
- 3.5 Threats
- 3.6 Technology Analysis
- 3.7 End User Analysis
- 3.8 Emerging Markets
- 3.9 Impact of Covid-19
- 4 Porters Five Force Analysis
- 4.1 Bargaining power of suppliers
- 4.2 Bargaining power of buyers
- 4.3 Threat of substitutes
- 4.4 Threat of new entrants
- 4.5 Competitive rivalry
- 5 Global AI-Powered Fraud Detection Market, By Component
- 5.1 Introduction
- 5.2 Software
- 5.2.1 AI/ML Fraud Detection Platforms
- 5.2.2 Real-Time Transaction Monitoring Tools
- 5.2.3 Identity Verification & Authentication Solutions
- 5.2.4 Risk & Compliance Management Modules
- 5.3 Services
- 5.3.1 Consulting & Advisory Services
- 5.3.2 Managed Services
- 5.3.3 Integration & Implementation Services
- 6 Global AI-Powered Fraud Detection Market, By Fraud Type
- 6.1 Introduction
- 6.2 Payment fraud
- 6.3 Identity theft and account takeover
- 6.4 Insurance fraud
- 6.5 Loan and credit fraud
- 6.6 E-commerce and retail fraud
- 6.7 Other Fraud Types
- 7 Global AI-Powered Fraud Detection Market, By Deployment Model
- 7.1 Introduction
- 7.2 On-premise
- 7.3 Cloud
- 8 Global AI-Powered Fraud Detection Market, By Organization Size
- 8.1 Introduction
- 8.2 Small and Medium Enterprises (SMEs)
- 8.3 Large Enterprises
- 9 Global AI-Powered Fraud Detection Market, By Technology
- 9.1 Introduction
- 9.2 Machine Learning and Deep Learning
- 9.3 Natural Language Processing
- 9.4 Behavioral Analytics
- 9.5 Predictive Analytics
- 9.6 Other Technologies
- 10 Global AI-Powered Fraud Detection Market, By End User
- 10.1 Introduction
- 10.2 Banking, Financial Services, and Insurance (BFSI)
- 10.3 Healthcare and Life Sciences
- 10.4 IT and Telecommunications
- 10.5 Government and Public Sector
- 10.6 Energy and Utilities
- 10.7 Other End Users
- 11 Global AI-Powered Fraud Detection Market, By Geography
- 11.1 Introduction
- 11.2 North America
- 11.2.1 US
- 11.2.2 Canada
- 11.2.3 Mexico
- 11.3 Europe
- 11.3.1 Germany
- 11.3.2 UK
- 11.3.3 Italy
- 11.3.4 France
- 11.3.5 Spain
- 11.3.6 Rest of Europe
- 11.4 Asia Pacific
- 11.4.1 Japan
- 11.4.2 China
- 11.4.3 India
- 11.4.4 Australia
- 11.4.5 New Zealand
- 11.4.6 South Korea
- 11.4.7 Rest of Asia Pacific
- 11.5 South America
- 11.5.1 Argentina
- 11.5.2 Brazil
- 11.5.3 Chile
- 11.5.4 Rest of South America
- 11.6 Middle East & Africa
- 11.6.1 Saudi Arabia
- 11.6.2 UAE
- 11.6.3 Qatar
- 11.6.4 South Africa
- 11.6.5 Rest of Middle East & Africa
- 12 Key Developments
- 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
- 12.2 Acquisitions & Mergers
- 12.3 New Product Launch
- 12.4 Expansions
- 12.5 Other Key Strategies
- 13 Company Profiling
- 13.1 IBM Corporation
- 13.2 SAS Institute Inc.
- 13.3 FICO (Fair Isaac Corporation)
- 13.4 BAE Systems plc
- 13.5 ACI Worldwide, Inc.
- 13.6 NICE Actimize
- 13.7 Experian plc
- 13.8 LexisNexis Risk Solutions
- 13.9 Kount, Inc.
- 13.10 Featurespace Ltd.
- 13.11 Feedzai, Inc.
- 13.12 Riskified Ltd.
- 13.13 Darktrace Holdings Ltd.
- 13.14 Mastercard Incorporated
- 13.15 Visa Inc.
- List of Tables
- Table 1 Global AI-Powered Fraud Detection Market Outlook, By Region (2024-2032) ($MN)
- Table 2 Global AI-Powered Fraud Detection Market Outlook, By Component (2024–2032) ($MN)
- Table 3 Global AI-Powered Fraud Detection Market Outlook, By Software (2024–2032) ($MN)
- Table 4 Global AI-Powered Fraud Detection Market Outlook, By AI/ML Fraud Detection Platforms (2024–2032) ($MN)
- Table 5 Global AI-Powered Fraud Detection Market Outlook, By Real-Time Transaction Monitoring Tools (2024–2032) ($MN)
- Table 6 Global AI-Powered Fraud Detection Market Outlook, By Identity Verification and Authentication Solutions (2024–2032) ($MN)
- Table 7 Global AI-Powered Fraud Detection Market Outlook, By Risk and Compliance Management Modules (2024–2032) ($MN)
- Table 8 Global AI-Powered Fraud Detection Market Outlook, By Services (2024–2032) ($MN)
- Table 9 Global AI-Powered Fraud Detection Market Outlook, By Consulting and Advisory Services (2024–2032) ($MN)
- Table 10 Global AI-Powered Fraud Detection Market Outlook, By Managed Services (2024–2032) ($MN)
- Table 11 Global AI-Powered Fraud Detection Market Outlook, By Integration and Implementation Services (2024–2032) ($MN)
- Table 12 Global AI-Powered Fraud Detection Market Outlook, By Fraud Type (2024–2032) ($MN)
- Table 13 Global AI-Powered Fraud Detection Market Outlook, By Payment Fraud (2024–2032) ($MN)
- Table 14 Global AI-Powered Fraud Detection Market Outlook, By Identity Theft and Account Takeover (2024–2032) ($MN)
- Table 15 Global AI-Powered Fraud Detection Market Outlook, By Insurance Fraud (2024–2032) ($MN)
- Table 16 Global AI-Powered Fraud Detection Market Outlook, By Loan and Credit Fraud (2024–2032) ($MN)
- Table 17 Global AI-Powered Fraud Detection Market Outlook, By E-Commerce and Retail Fraud (2024–2032) ($MN)
- Table 18 Global AI-Powered Fraud Detection Market Outlook, By Other Fraud Types (2024–2032) ($MN)
- Table 19 Global AI-Powered Fraud Detection Market Outlook, By Deployment Model (2024–2032) ($MN)
- Table 20 Global AI-Powered Fraud Detection Market Outlook, By On-premise (2024–2032) ($MN)
- Table 21 Global AI-Powered Fraud Detection Market Outlook, By Cloud (2024–2032) ($MN)
- Table 22 Global AI-Powered Fraud Detection Market Outlook, By Organization Size (2024–2032) ($MN)
- Table 23 Global AI-Powered Fraud Detection Market Outlook, By Small and Medium Enterprises (SMEs) (2024–2032) ($MN)
- Table 24 Global AI-Powered Fraud Detection Market Outlook, By Large Enterprises (2024–2032) ($MN)
- Table 25 Global AI-Powered Fraud Detection Market Outlook, By Technology (2024–2032) ($MN)
- Table 26 Global AI-Powered Fraud Detection Market Outlook, By Machine Learning and Deep Learning (2024–2032) ($MN)
- Table 27 Global AI-Powered Fraud Detection Market Outlook, By Natural Language Processing (2024–2032) ($MN)
- Table 28 Global AI-Powered Fraud Detection Market Outlook, By Behavioral Analytics (2024–2032) ($MN)
- Table 29 Global AI-Powered Fraud Detection Market Outlook, By Predictive Analytics (2024–2032) ($MN)
- Table 30 Global AI-Powered Fraud Detection Market Outlook, By Other Technologies (2024–2032) ($MN)
- Table 31 Global AI-Powered Fraud Detection Market Outlook, By End User (2024–2032) ($MN)
- Table 32 Global AI-Powered Fraud Detection Market Outlook, By Banking, Financial Services, and Insurance (BFSI) (2024–2032) ($MN)
- Table 33 Global AI-Powered Fraud Detection Market Outlook, By Healthcare and Life Sciences (2024–2032) ($MN)
- Table 34 Global AI-Powered Fraud Detection Market Outlook, By IT and Telecommunications (2024–2032) ($MN)
- Table 35 Global AI-Powered Fraud Detection Market Outlook, By Government and Public Sector (2024–2032) ($MN)
- Table 36 Global AI-Powered Fraud Detection Market Outlook, By Energy and Utilities (2024–2032) ($MN)
- Table 37 Global AI-Powered Fraud Detection Market Outlook, By Other End Users (2024–2032) ($MN)
- Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
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