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市场调查报告书
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1925024

人工智慧驱动的诈欺侦测市场预测至2032年：按组件、诈欺类型、部署模式、组织规模、技术、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球人工智慧驱动的诈欺侦测市场规模将达到 142 亿美元，到 2032 年将达到 482 亿美元，预测期内的复合年增长率为 19%。

人工智慧驱动的诈欺侦测是一种技术主导方法，它利用人工智慧、机器学习和进阶分析技术，即时识别、预防和应对诈欺活动。透过分析海量的结构化和非结构化数据，人工智慧系统能够侦测出可能预示诈欺的异常模式和可疑行为。这些系统能够持续学习并从新数据中适应，从而不断提高其准确性。人工智慧驱动的诈欺侦测技术已广泛应用于银行、电子商务、保险和网路安全等领域，有助于保护交易安全、减少经济损失并提升信任。与传统方法相比，它能够提供更快、更有效率、更主动的欺诈管理。

金融领域的网路犯罪日益猖獗

金融机构需要先进的系统来保护交易和客户身分。人工智慧驱动的平台透过即时分析海量资料集来加速诈欺侦测。供应商正透过整合能够适应不断演变的威胁的机器学习演算法来加速这项技术的普及。对安全金融生态系统日益增长的需求正在推动银行业、保险业和金融科技业采用人工智慧驱动的诈欺检测技术。企业正在投资人工智慧驱动的诈欺侦测，以加强合规性和营运可靠性。金融领域网路犯罪的日益猖獗，使得人工智慧驱动的诈欺侦测成为数位安全的关键基础。

人工智慧安全专业知识有限

企业难以找到管理复杂人工智慧驱动平台所需的人才。与资源雄厚的成熟企业相比，小规模企业受制于人才短缺。高级分析日益复杂，进一步阻碍了技术的推广倡议。供应商正大力推广简化的介面和自动化功能，以减少对专业技能的依赖。持续的人才短缺限制了扩充性，并延长了现代化进程。人才短缺正在重塑技术推广策略，技能发展成为成功的关键因素。

与云端运算和区块链技术的集成

企业需要一个安全的框架来保护分散式资料和数位交易。云端原生平台支援跨混合环境的可扩展诈欺侦测，进而提升敏捷性。供应商正透过将基于区块链的透明度和不可篡改的记录融入诈欺预防系统进行创新。对数位转型的持续投入正在推动银行、金融和保险 (BFSI) 以及电信生态系统的需求成长。云端和区块链的融合正在加速诈欺检测，使其成为一种主动的安全连接手段。这些技术的蓬勃发展使人工智慧驱动的诈欺检测成为数位经济信任的基石。

快速演变且日益复杂的网路攻击

组织机构面临着来自高级身份盗窃和基于凭证的入侵的日益增长的风险。资源有限限制了小规模供应商应对高级攻击手段的能力。法规结构增加了复杂性，并阻碍了部署策略。供应商透过整合加密、行为分析和合规功能来降低风险。网路攻击的复杂性正在削弱信任，并将重点转向增强韧性。进阶诈欺技术正在重新定义人工智慧驱动的检测技术，使其成为抵御不断演变的数位威胁的第一道防线。

新冠疫情的感染疾病：

新冠疫情导致数位交易激增，推动了对人工智慧驱动的诈欺检测的需求。一方面，劳动力和供应链中断阻碍了计划的实施；另一方面，对安全远端金融服务的需求成长加速了人工智慧平台的普及。为了在动盪的环境下维持运营，企业更加依赖即时监控和自适应分析。供应商整合了先进的自动化和合规功能，以增强系统的韧性。新冠疫情凸显了人工智慧驱动的诈欺检测在金融生态系统中作为信任和持续营运关键基础的重要性。

预计在预测期内，银行、金融服务和保险（BFSI）行业将占据最大的市场份额。

在对可扩展诈欺侦测框架的需求驱动下，银行、金融服务和保险 (BFSI) 行业预计将在预测期内占据最大的市场份额。各公司正在将人工智慧平台融入其工作流程，以加快合规速度并增强交易安全性。供应商正在开发整合自动化、分析和身份验证功能的解决方案。对安全、数位化优先营运日益增长的需求正在推动该行业的应用。 BFSI 机构认识到，诈欺侦测对于维护消费者信任和营运诚信至关重要。人工智慧系统正在增强诈欺侦测能力，从而为财务韧性奠定基础。

预计在预测期内，身分盗窃和帐户盗用领域的复合年增长率将最高。

在安全身分管理需求不断增长的推动下，身分盗窃和帐户盗用领域预计将在预测期内实现最高成长率。金融机构正在加速采用人工智慧驱动的系统来保护客户帐户和数位身分。供应商正在整合自适应身份验证和行为分析功能，以提高响应速度。中小企业和大型企业都受益于能够应对各种诈欺场景的扩充性解决方案。对安全交易框架的投资不断增加，正在推动该领域的需求。身分盗窃预防正在促进诈欺侦测，从而成为保护消费者权益的催化剂。

占比最大的地区：

预计在预测期内，北美将保持最大的市场份额，这主要得益于其成熟的金融基础设施以及企业对诈欺检测框架的广泛应用。美国和加拿大的企业正在加速对人工智慧平台的投资。领先技术提供者的存在进一步巩固了该地区的领先地位。对资料隐私法规合规性的日益增长的需求正在推动各行业的应用。供应商正在整合先进的自动化和分析功能，以在竞争激烈的市场中脱颖而出。北美的领先地位体现在其能够将诈欺侦测领域的创新与监管合规的严谨性完美融合。

复合年增长率最高的地区：

亚太地区预计将在预测期内实现最高的复合年增长率，这主要得益于快速的数位化、不断增长的行动网路普及率以及政府主导的普惠金融倡议。中国、印度和东南亚等国家正在加速投资人工智慧驱动的诈欺检测技术，以支援业务成长。本地Start-Ups正在推出针对不同消费族群量身订製的高性价比解决方案。企业正在采用人工智慧驱动的云端原生平台，以提高可扩展性并满足合规要求。政府推行的数位转型计画正在推动这些技术的应用。亚太地区的成长动力源自于不断演变的诈欺风险，使其成为诈欺侦测创新领域最具适应性的中心。

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    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟进行基准分析

目录

第一章执行摘要

第二章 前言

  • 概括
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球人工智慧驱动的诈欺侦测市场（按组件划分）

  • 软体
    • 人工智慧/机器学习（ML）诈欺侦测平台
    • 即时交易监控工具
    • 身份验证和认证解决方案
    • 风险与合规管理模组
  • 服务
    • 咨询和顾问服务
    • 託管服务
    • 整合和实施服务

6. 全球人工智慧驱动的诈欺侦测市场（按诈欺类型划分）

  • 支付诈骗
  • 身分盗窃和帐户劫持
  • 保险诈欺
  • 贷款和信贷诈骗
  • 电子商务与零售诈骗
  • 其他的

7. 全球人工智慧驱动的诈欺侦测市场（按部署模式划分）

  • 本地部署
  • 云

8. 全球人工智慧驱动的诈欺侦测市场（按组织规模划分）

  • 中小企业
  • 大公司

9. 全球人工智慧驱动的诈欺侦测市场（按技术划分）

  • 机器学习和深度学习
  • 自然语言处理
  • 行为分析
  • 预测分析
  • 其他的

第十章：全球人工智慧驱动的诈欺侦测市场（按最终用户划分）

  • 银行、金融服务和保险（BFSI）
  • 医疗保健和生命科学
  • 资讯科技/通讯
  • 政府/公共部门
  • 能源与公共产业
  • 其他的

第十一章 全球人工智慧驱动的诈欺侦测市场（按地区划分）

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十三章：企业概况

  • 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.