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市場調查報告書
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1925024

人工智慧驅動的詐欺偵測市場預測至2032年：按組件、詐欺類型、部署模式、組織規模、技術、最終用戶和地區分類的全球分析

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
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球人工智慧驅動的詐欺偵測市場規模將達到 142 億美元，到 2032 年將達到 482 億美元，預測期內的複合年成長率為 19%。

人工智慧驅動的詐欺偵測是一種技術主導方法，它利用人工智慧、機器學習和進階分析技術，即時識別、預防和應對詐欺活動。透過分析海量的結構化和非結構化數據，人工智慧系統能夠偵測出可能預示詐欺的異常模式和可疑行為。這些系統能夠持續學習並從新數據中適應，從而不斷提高其準確性。人工智慧驅動的詐欺偵測技術已廣泛應用於銀行、電子商務、保險和網路安全等領域，有助於保護交易安全、減少經濟損失並提升信任。與傳統方法相比，它能夠提供更快、更有效率、更主動的欺詐管理。

金融領域的網路犯罪日益猖獗

金融機構需要先進的系統來保護交易和客戶身分。人工智慧驅動的平台透過即時分析海量資料集來加速詐欺偵測。供應商正透過整合能夠適應不斷演變的威脅的機器學習演算法來加速這項技術的普及。對安全金融生態系統日益成長的需求正在推動銀行業、保險業和金融科技業採用人工智慧驅動的詐欺檢測技術。企業正在投資人工智慧驅動的詐欺偵測，以加強合規性和營運可靠性。金融領域網路犯罪的日益猖獗，使得人工智慧驅動的詐欺偵測成為數位安全的關鍵基礎。

人工智慧安全專業知識有限

企業難以找到管理複雜人工智慧驅動平台所需的人才。與資源雄厚的成熟企業相比，小規模企業受制於人才短缺。高級分析日益複雜，進一步阻礙了技術的推廣舉措。供應商正大力推廣簡化的介面和自動化功能，以減少對專業技能的依賴。持續的人才短缺限制了擴充性，並延長了現代化進程。人才短缺正在重塑技術推廣策略，技能發展成為成功的關鍵因素。

與雲端運算和區塊鏈技術的整合

企業需要一個安全的框架來保護分散式資料和數位交易。雲端原生平台支援跨混合環境的可擴展詐欺偵測，進而提升敏捷性。供應商正透過將基於區塊鏈的透明度和不可篡改的記錄融入詐欺預防系統進行創新。對數位轉型的持續投入正在推動銀行、金融和保險 (BFSI) 以及電信生態系統的需求成長。雲端和區塊鏈的融合正在加速詐欺檢測，使其成為一種主動的安全連接手段。這些技術的蓬勃發展使人工智慧驅動的詐欺檢測成為數位經濟信任的基石。

快速演變且日益複雜的網路攻擊

組織機構面臨著來自高級身份盜竊和基於憑證的入侵的日益成長的風險。資源有限限制了小規模供應商應對高級攻擊手段的能力。法規結構增加了複雜性，並阻礙了部署策略。供應商透過整合加密、行為分析和合規功能來降低風險。網路攻擊的複雜性正在削弱信任，並將重點轉向增強韌性。進階詐欺技術正在重新定義人工智慧驅動的檢測技術，使其成為抵禦不斷演變的數位威脅的第一道防線。

新冠疫情的感染疾病：

新冠疫情導致數位交易激增，推動了對人工智慧驅動的詐欺檢測的需求。一方面，勞動力和供應鏈中斷阻礙了計劃的實施；另一方面，對安全遠端金融服務的需求成長加速了人工智慧平台的普及。為了在動盪的環境下維持運營，企業更加依賴即時監控和自適應分析。供應商整合了先進的自動化和合規功能，以增強系統的韌性。新冠疫情凸顯了人工智慧驅動的詐欺檢測在金融生態系統中作為信任和持續營運關鍵基礎的重要性。

預計在預測期內，銀行、金融服務和保險（BFSI）行業將佔據最大的市場佔有率。

在對可擴展詐欺偵測框架的需求驅動下，銀行、金融服務和保險 (BFSI) 行業預計將在預測期內佔據最大的市場佔有率。各公司正在將人工智慧平台融入其工作流程，以加快合規速度並增強交易安全性。供應商正在開發整合自動化、分析和身份驗證功能的解決方案。對安全、數位化優先營運日益成長的需求正在推動該行業的應用。 BFSI 機構認知到，詐欺偵測對於維護消費者信任和營運誠信至關重要。人工智慧系統正在增強詐欺偵測能力，從而為財務韌性奠定基礎。

預計在預測期內，身分盜竊和帳戶盜用領域的複合年成長率將最高。

在安全身分管理需求不斷成長的推動下，身分盜竊和帳戶盜用領域預計將在預測期內實現最高成長率。金融機構正在加速採用人工智慧驅動的系統來保護客戶帳戶和數位身分。供應商正在整合自適應身份驗證和行為分析功能，以提高響應速度。中小企業和大型企業都受益於能夠應對各種詐欺場景的擴充性解決方案。對安全交易框架的投資不斷增加，正在推動該領域的需求。身分盜竊預防正在促進詐欺偵測，從而成為保護消費者權益的催化劑。

佔比最大的地區：

預計在預測期內，北美將保持最大的市場佔有率，這主要得益於其成熟的金融基礎設施以及企業對詐欺檢測框架的廣泛應用。美國和加拿大的企業正在加速對人工智慧平台的投資。領先技術提供者的存在進一步鞏固了該地區的領先地位。對資料隱私法規合規性的日益成長的需求正在推動各行業的應用。供應商正在整合先進的自動化和分析功能，以在競爭激烈的市場中脫穎而出。北美的領先地位體現在其能夠將詐欺偵測領域的創新與監管合規的嚴謹性完美融合。

複合年成長率最高的地區：

亞太地區預計將在預測期內實現最高的複合年成長率，這主要得益於快速的數位化、不斷成長的行動網路普及率以及政府主導的普惠金融舉措。中國、印度和東南亞等國家正在加速投資人工智慧驅動的詐欺檢測技術，以支援業務成長。本地Start-Ups正在推出針對不同消費族群量身訂製的具成本效益解決方案。企業正在採用人工智慧驅動的雲端原生平台，以提高可擴展性並滿足合規要求。政府推行的數位轉型計畫正在推動這些技術的應用。亞太地區的成長動力源自於不斷演變的詐欺風險，使其成為詐欺偵測創新領域最具適應性的中心。

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目錄

第1章執行摘要

第2章 前言

  • 概括
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧驅動的詐欺偵測市場（按組件分類）

  • 軟體
    • 人工智慧/機器學習（ML）詐欺偵測平台
    • 即時交易監控工具
    • 身份驗證和認證解決方案
    • 風險與合規管理模組
  • 服務
    • 諮詢和顧問服務
    • 託管服務
    • 整合和實施服務

6. 全球人工智慧驅動的詐欺偵測市場（按詐欺類型分類）

  • 支付詐騙
  • 身分盜竊和帳戶劫持
  • 保險詐欺
  • 貸款和信貸詐騙
  • 電子商務與零售詐騙
  • 其他

7. 全球人工智慧驅動的詐欺偵測市場（按部署模式分類）

  • 本地部署
  • 雲

8. 全球人工智慧驅動的詐欺偵測市場（按組織規模分類）

  • 中小企業
  • 主要企業

9. 全球人工智慧驅動的詐欺偵測市場（按技術分類）

  • 機器學習和深度學習
  • 自然語言處理
  • 行為分析
  • 預測分析
  • 其他

第10章：全球人工智慧驅動的詐欺偵測市場（按最終用戶分類）

  • 銀行、金融服務和保險（BFSI）
  • 醫療保健和生命科學
  • 資訊科技/通訊
  • 政府/公共部門
  • 能源與公共產業
  • 其他

第11章 全球人工智慧驅動的詐欺偵測市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第13章：企業概況

  • 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
  • Visa Inc.
Product Code: SMRC33418

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(R) Viya(R) 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

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

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.