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市场调查报告书
商品编码
1798085

2032 年人工智慧诈欺侦测与预防市场预测：按组件、部署类型、组织规模、技术、应用、最终用户和地区进行的全球分析

AI for Fraud Detection & Prevention Market Forecasts to 2032 - Global Analysis By Component (Solution and Services), Deployment Mode (Cloud, On-Premises and Hybrid), Organization Size, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球用于诈欺侦测和预防的人工智慧市场预计将在 2025 年达到 149.1 亿美元，到 2032 年将达到 536.2 亿美元，预测期内的复合年增长率为 20.06%。

用于诈欺检测和预防的人工智慧利用数据分析和先进的机器学习演算法，即时发现可疑活动、趋势和异常。透过分析大量交易、行为和历史数据，人工智慧系统能够比传统方法更快、更准确地识别潜在诈欺行为。透过异常检测、预测建模和自然语言处理等技术，网路安全团队、电商平台和金融机构可以改善决策，减少误报，并预测诈欺活动。由于人工智慧能够持续从新数据中学习，随着诈骗手段日益复杂，诈骗防制也变得更加主动、灵活和有效。

根据 BioCatch 行为生物识别协会的数据，74% 的金融机构已经在使用人工智慧来检测金融犯罪，73% 的金融机构正在使用人工智慧来检测欺诈，这表明人工智慧主导的安全框架得到了广泛的采用和组织信任。

日益增长的网路威胁和先进的诈骗手段

网路威胁日益复杂，包括深度造假、网路钓鱼、冒充和合成诈骗，这推动了对更智慧安全解决方案的需求。传统的基于规则的系统无法识别细微且不断演变的诈欺模式，常常导致重大的财务和声誉损失。人工智慧主导的平台使用行为分析、异常检测和机器学习来持续分析大型资料集并适应新出现的威胁。透过即时检测异常行为并从历史模式中学习，人工智慧可以降低风险敞口并实现主动干预。此外，随着诈骗变得越来越老练，人工智慧的预测能力对于保护通讯、电子商务和金融服务领域的数位生态系统至关重要。

实施和维护成本高

部署人工智慧诈欺侦测系统需要在软体、硬体和专业人才方面进行大量的初始投资。人工智慧平台必须与组织现有的IT基础设施频繁集成，这既具有挑战性，又成本高昂。此外，维护此类系统需要持续监控、更新和重新训练人工智慧模型，以适应诈骗策略的变化。这些成本可能会限制其采用，对于中小型企业来说，成本过高。儘管人工智慧的优势显而易见，但高昂的成本会延迟其采用，降低投资收益，并阻碍一些公司全面采用人工智慧诈欺预防技术。

电子商务和数位支付的使用日益增多

在全球范围内，数位银行、行动钱包和网路购物的快速发展导致数位交易量激增。由于传统方法无法应对高频、多通路交易，这种扩张为基于人工智慧的诈欺侦测系统提供了巨大的机会。人工智慧可以即时分析大量数据，并在诈欺、异常模式和潜在诈骗影响客户和企业之前识别它们。为了维护消费者信任并最大限度地减少财务损失，电子商务平台、金融科技Start-Ups和数位支付提供商正在增加对人工智慧的投资。随着数位交易的持续成长，对强大的人工智慧诈欺预防解决方案的需求预计将快速增长。

解决方案提供者之间的激烈竞争

人工智慧诈欺侦测市场竞争日益激烈，许多国内外供应商提供的解决方案千差万别。为了在激烈的竞争中吸引并留住客户，企业不断面临创新、降低价格和提升服务品质的压力。规模较小的公司难以与拥有更丰富资源和更成熟技术的老牌供应商竞争，而新参与企业则可能难以建立信誉和信任。此外，这种竞争环境可能会减缓整体市场成长，增加行销和研发费用，并降低利润率。为了维持市场占有率并维持长期成长，企业必须透过尖端技术、一流的客户服务和策略合作伙伴关係来脱颖而出。

COVID-19的影响

新冠疫情大大加速了众多产业的数位转型，导致线上交易、远距银行、电子商务和数位支付激增，也增加了诈骗的可能性。由于传统方法无法应对线上交易的数量和复杂性，这种快速变化推动了对基于人工智慧的诈欺检测和预防解决方案的需求。为了确保业务连续性和客户信任，企业迅速采用人工智慧技术来即时监控、分析和回应可疑活动。此外，疫情凸显了对云端基础的可扩展人工智慧系统的需求，这些系统能够适应新的诈欺趋势和快速发展的数位行为。

预计云端运算市场将成为预测期内最大的市场

预计云端技术将在预测期内占据最大的市场占有率。这得益于云端解决方案的可扩展性、成本效益和灵活性，使企业能够快速适应不断变化的诈欺策略。云端基础的平台支援即时数据处理和多通路集成，进而提升诈欺侦测和预防能力。此外，由云端的集中式基础设施支援的先进人工智慧模型、机器学习演算法和行为分析对于发现复杂的诈欺趋势至关重要。这些特性使云端技术成为希望在不牺牲营运灵活性的情况下改善诈欺侦测系统的企业的理想选择。

预计机器学习领域在预测期内将以最高复合年增长率成长

预计机器学习领域将在预测期内实现最高成长率。机器学习可实现即时诈欺侦测，广泛应用于在海量资料集中查找模式和异常。机器学习 (ML) 系统利用不断从交易和历史资料中学习的演算法，从而能够预测和预防欺诈，并随着时间的推移提高准确性。该领域因其在银行、电子商务、保险和通讯等领域的多功能性而引领市场。此外，机器学习能够最大限度地减少误报、自动化诈欺侦测程序并提高决策效率，使其成为现代反诈欺解决方案的重要组成部分。

比最大的地区

预计北美地区将在预测期内占据最大的市场占有率。该地区较高的数位支付渗透率、先进的技术基础设施以及IBM、微软和甲骨文等主要参与者的存在，正在推动欺诈检测解决方案领域的竞争和创新，是其主导的关键因素。数位交易的兴起和网路威胁的日益复杂，尤其使美国处于领先地位。此外，机器学习和深度学习等人工智慧技术的整合显着提升了诈欺侦测系统的能力，使北美目前在该领域处于领先地位。

复合年增长率最高的地区

预计亚太地区在预测期内的复合年增长率最高。中国、印度、日本、澳洲和东南亚国家等主要经济体的快速数位转型是这一强劲成长的主要驱动力。数位钱包、电子商务、网路银行和行动支付系统的快速普及，推动了数位交易生态系统的快速发展。此外，诈欺和网路攻击的风险也不断增加。为了保护业务和客户讯息，该领域的公司越来越多地采用基于人工智慧的诈欺检测系统。

免费客製化服务

订阅此报告的客户可享有以下免费自订选项之一：

  • 公司简介
    • 对最多三家其他市场参与企业进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 透过产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 二手研究资料
    • 先决条件

第三章市场走势分析

  • 介绍
  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

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. 全球人工智慧诈欺侦测与预防市场（按组件）

  • 介绍
  • 解决方案
  • 服务

6. 全球诈欺侦测与预防人工智慧市场（按部署类型）

  • 介绍
  • 云
  • 本地
  • 杂交种

第七章诈骗侦测与预防人工智慧市场（依组织规模）

  • 介绍
  • 小型企业
  • 大公司

第 8 章 人工智慧诈骗侦测与预防市场（按技术）

  • 介绍
  • 机器学习
  • 深度学习
  • 自然语言处理
  • 图形分析
  • 联邦学习和隐私保护人工智慧
  • 其他的

9. 全球人工智慧诈欺侦测与预防市场（按应用）

  • 介绍
  • 交易监控
  • 身份盗窃侦测
  • 帐户盗用预防
  • 支付诈骗检测
  • 保险诈骗
  • 反洗钱（AML）
  • 行为生物识别
  • 合成身份检测
  • 其他的

10. 全球诈欺侦测与预防人工智慧市场（按最终用户）

  • 介绍
  • 银行、金融服务和保险（BFSI）
  • 政府和公共部门
  • 卫生保健
  • 资讯科技和通讯
  • 製造业
  • 活力
  • 其他的

11. 全球人工智慧诈欺侦测与预防市场（按地区）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 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
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章：公司概况

  • IBM Corporation
  • BAE Systems
  • ACI Worldwide Inc
  • Fiserv Inc
  • Mastercard Inc
  • Feedzai Inc
  • Oracle Inc
  • Experian Inc
  • Cisco
  • Lexis Nexis Risk Solutions Inc
  • NOOS Technologies Inc
  • Forter Inc
  • Onfido Inc
  • PayPal
  • Abrigo Inc
Product Code: SMRC30455

According to Stratistics MRC, the Global AI for Fraud Detection & Prevention Market is accounted for $14.91 billion in 2025 and is expected to reach $53.62 billion by 2032 growing at a CAGR of 20.06% during the forecast period. AI for fraud detection and prevention uses data analytics and sophisticated machine learning algorithms to instantly spot suspicious activity, trends, and anomalies. Large volumes of transactional, behavioral, and historical data can be analyzed by AI systems to identify possible fraud more quickly and accurately than with conventional techniques. Using methods like anomaly detection, predictive modeling, and natural language processing, cyber security teams, e-commerce platforms, and financial institutions can improve decision-making, reduce false positives, and predict fraudulent activity. Because AI is constantly learning from new data, fraud prevention becomes more proactive, flexible, and effective as fraud schemes become more complex.

According to BioCatch Behavioral Biometrics Association, 74% of financial institutions are already using AI for financial-crime detection and 73% for fraud detection, indicating widespread adoption and institutional trust in AI-driven security frameworks.

Market Dynamics:

Driver:

Growing cyber threats and advanced fraud techniques

The need for more intelligent security solutions has increased due to the complexity of cyber threats, such as deep fakes, phishing, identity theft, and synthetic fraud. The inability of traditional rule-based systems to identify subtle or changing fraudulent patterns frequently results in large losses in terms of money and reputation. Behavioral analytics, anomaly detection, and machine learning are used by AI-driven platforms to continuously analyze large datasets and adjust to new threats. AI makes proactive intervention possible by detecting anomalous behaviors in real-time and learning from past patterns, lowering risk exposure. Moreover, artificial intelligence's predictive powers are essential for protecting digital ecosystems in telecommunications, e-commerce, and financial services as fraudsters get more complex.

Restraint:

High costs of implementation and upkeep

The implementation of AI-powered fraud detection systems necessitates a large initial investment in software, hardware, and qualified staff. AI platforms must frequently be integrated with an organization's current IT infrastructure, which can be difficult and expensive. Additionally, in order to maintain these systems, AI models must be continuously monitored, updated, and retrained to keep up with changing fraud strategies. Adoption may be restricted by such costs, which can be prohibitive for small and medium-sized businesses. Despite its obvious advantages, high costs can cause deployment delays, lower return on investment, and discourage some businesses from fully implementing AI-driven fraud prevention.

Opportunity:

Growing use of e-commerce and digital payments

Globally, the volume of digital transactions is soaring due to the quick development of digital banking, mobile wallets, and online shopping. Due to traditional methods' inability to handle high-frequency, multi-channel transactions, this expansion present a huge opportunity for AI-driven fraud detection systems. AI is capable of real-time analysis of enormous volumes of data, identifying irregularities, odd patterns, and possible fraud before it affects clients or companies. In order to preserve consumer confidence and minimize financial losses, e-commerce platforms, fintech startups, and digital payment providers are investing more and more in AI. Additionally, the need for strong AI fraud prevention solutions is expected to grow rapidly as digital transactions continue to increase.

Threat:

Strong rivalry between solution providers

The market for AI fraud detection is getting more and more crowded, with many local and international vendors providing overlapping solutions. Businesses are under constant pressure to innovate, lower prices, and improve service quality in order to draw in and keep customers in the face of fierce competition. Established vendors with greater resources and sophisticated technology stacks may be harder for smaller players to compete with, and newcomers may encounter difficulties establishing credibility and trust. Furthermore, this competitive environment can slow market growth overall, raise marketing and R&D expenses, and lower profit margins. To preserve market share and maintain long-term growth, businesses must set themselves apart through cutting-edge features, first-rate customer service, or strategic alliances.

Covid-19 Impact:

The COVID-19 pandemic dramatically sped up digital transformation in many industries, increasing the likelihood of fraudulent activity by causing a spike in online transactions, remote banking, e-commerce, and digital payments. Due to traditional methods' inability to handle the volume and complexity of online transactions, this abrupt shift increased demand for AI-powered fraud detection and prevention solutions. In order to ensure business continuity and customer trust, organizations swiftly embraced AI technologies to monitor, analyze, and react to suspicious activities in real time. Moreover, the pandemic also highlighted the need for cloud-based, scalable AI systems that can adjust to new fraud trends and quickly evolving digital behaviors.

The cloud segment is expected to be the largest during the forecast period

The cloud segment is expected to account for the largest market share during the forecast period. This preference stems from cloud solutions' scalability, cost-effectiveness, and flexibility, which allow businesses to swiftly adjust to changing fraud strategies. Cloud-based platforms improve the detection and prevention of fraudulent activities by enabling real-time data processing and integration across multiple channels. Furthermore, sophisticated AI models, machine learning algorithms, and behavioral analytics are supported by the cloud's centralized infrastructure and are essential for spotting intricate fraud trends. Because of these features, cloud deployment is the go-to option for companies looking to improve their fraud detection systems without sacrificing operational flexibility.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate. Real-time fraud detection is made possible by machine learning, which is widely used to find patterns and anomalies in massive datasets. Over time, machine learning (ML) systems can predict and prevent fraud with ever-increasing accuracy by utilizing algorithms that continuously learn from transactional and historical data. Because of its versatility across sectors like banking, e-commerce, insurance, and telecommunications, this segment leads the market. Moreover, machine learning is a key component of contemporary fraud prevention solutions due to its capacity to minimize false positives, automate fraud detection procedures, and improve decision-making effectiveness.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. The region's high rates of digital payment method adoption, sophisticated technological infrastructure, and the presence of big players like IBM, Microsoft, and Oracle-all of which encourage competition and innovation in fraud detection solutions-are the main causes of this dominance. Due to an increase in digital transactions and the sophistication of cyber threats, the United States in particular has been at the forefront. Additionally, North America is now a leader in this field owing to the integration of AI technologies, such as machine learning and deep learning, which have greatly improved the capabilities of fraud detection systems.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid digital transformation in important economies like China, India, Japan, Australia, and Southeast Asian nations is the main driver of this strong growth. The ecosystem of digital transactions has grown dramatically as a result of the quick uptake of digital wallets, e-commerce, online banking, and mobile payment systems. Furthermore, this has also increased the risk of fraud and cyberattacks. In order to protect their business operations and client information, companies in the area are progressively implementing AI-based fraud detection systems.

Key players in the market

Some of the key players in AI for Fraud Detection & Prevention Market include IBM Corporation, BAE Systems, ACI Worldwide Inc, Fiserv Inc, Mastercard Inc, Feedzai Inc, Oracle Inc, Experian Inc, Cisco, Lexis Nexis Risk Solutions Inc, NOOS Technologies Inc, Forter Inc, Onfido Inc, PayPal and Abrigo Inc.

Key Developments:

In June 2025, BAE Systems has signed a new contract with the Swedish Defence Materiel Administration to supply additional BONUS precision-guided munitions to the Swedish Armed Forces. This contract marks a continued partnership between BAE Systems Bofors and the Swedish Armed Forces, reinforcing their shared commitment to delivering cutting-edge defense solutions.

In April 2025, IBM and Tokyo Electron (TEL) announced an extension of their agreement for the joint research and development of advanced semiconductor technologies. The new 5-year agreement will focus on the continued advancement of technology for next-generation semiconductor nodes and architectures to power the age of generative AI.

In March 2025, ACI Worldwide has announced an extension of their strategic technology partnership. The agreement will see Co-op continue to use the full range of solutions offered by ACI's Payments Orchestration Platform, including in-store, online and mobile payment processing as well as end-to-end payments and fraud management.

Components Covered:

  • Solution
  • Services

Deployment Modes Covered:

  • Cloud
  • On-Premises
  • Hybrid

Organization Sizes Covered:

  • Small & Medium Enterprises
  • Large Enterprises

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Graph Analytics
  • Federated Learning & Privacy-Preserving AI
  • Other Technologies

Applications Covered:

  • Transaction Monitoring
  • Identity Theft Detection
  • Account Takeover Prevention
  • Payment Fraud Detection
  • Insurance Claim Fraud
  • Anti-Money Laundering (AML)
  • Behavioral Biometrics
  • Synthetic Identity Detection
  • Other Applications

End Users Covered:

  • Banking, Financial Services, and Insurance (BFSI)
  • Government and Public Sector
  • Healthcare
  • IT and Telecommunications
  • Manufacturing
  • Energy
  • 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 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 for Fraud Detection & Prevention Market, By Component

  • 5.1 Introduction
  • 5.2 Solution
  • 5.3 Services

6 Global AI for Fraud Detection & Prevention Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud
  • 6.3 On-Premises
  • 6.4 Hybrid

7 Global AI for Fraud Detection & Prevention Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Small & Medium Enterprises
  • 7.3 Large Enterprises

8 Global AI for Fraud Detection & Prevention Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning
  • 8.3 Deep Learning
  • 8.4 Natural Language Processing
  • 8.5 Graph Analytics
  • 8.6 Federated Learning & Privacy-Preserving AI
  • 8.7 Other Technologies

9 Global AI for Fraud Detection & Prevention Market, By Application

  • 9.1 Introduction
  • 9.2 Transaction Monitoring
  • 9.3 Identity Theft Detection
  • 9.4 Account Takeover Prevention
  • 9.5 Payment Fraud Detection
  • 9.6 Insurance Claim Fraud
  • 9.7 Anti-Money Laundering (AML)
  • 9.8 Behavioral Biometrics
  • 9.9 Synthetic Identity Detection
  • 9.10 Other Applications

10 Global AI for Fraud Detection & Prevention Market, By End User

  • 10.1 Introduction
  • 10.2 Banking, Financial Services, and Insurance (BFSI)
  • 10.3 Government and Public Sector
  • 10.4 Healthcare
  • 10.5 IT and Telecommunications
  • 10.6 Manufacturing
  • 10.7 Energy
  • 10.8 Other End Users

11 Global AI for Fraud Detection & Prevention 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 BAE Systems
  • 13.3 ACI Worldwide Inc
  • 13.4 Fiserv Inc
  • 13.5 Mastercard Inc
  • 13.6 Feedzai Inc
  • 13.7 Oracle Inc
  • 13.8 Experian Inc
  • 13.9 Cisco
  • 13.10 Lexis Nexis Risk Solutions Inc
  • 13.11 NOOS Technologies Inc
  • 13.12 Forter Inc
  • 13.13 Onfido Inc
  • 13.14 PayPal
  • 13.15 Abrigo Inc

List of Tables

  • Table 1 Global AI for Fraud Detection & Prevention Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI for Fraud Detection & Prevention Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI for Fraud Detection & Prevention Market Outlook, By Solution (2024-2032) ($MN)
  • Table 4 Global AI for Fraud Detection & Prevention Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI for Fraud Detection & Prevention Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 6 Global AI for Fraud Detection & Prevention Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 7 Global AI for Fraud Detection & Prevention Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 8 Global AI for Fraud Detection & Prevention Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 9 Global AI for Fraud Detection & Prevention Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 10 Global AI for Fraud Detection & Prevention Market Outlook, By Small & Medium Enterprises (2024-2032) ($MN)
  • Table 11 Global AI for Fraud Detection & Prevention Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 12 Global AI for Fraud Detection & Prevention Market Outlook, By Technology (2024-2032) ($MN)
  • Table 13 Global AI for Fraud Detection & Prevention Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 14 Global AI for Fraud Detection & Prevention Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 15 Global AI for Fraud Detection & Prevention Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 16 Global AI for Fraud Detection & Prevention Market Outlook, By Graph Analytics (2024-2032) ($MN)
  • Table 17 Global AI for Fraud Detection & Prevention Market Outlook, By Federated Learning & Privacy-Preserving AI (2024-2032) ($MN)
  • Table 18 Global AI for Fraud Detection & Prevention Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 19 Global AI for Fraud Detection & Prevention Market Outlook, By Application (2024-2032) ($MN)
  • Table 20 Global AI for Fraud Detection & Prevention Market Outlook, By Transaction Monitoring (2024-2032) ($MN)
  • Table 21 Global AI for Fraud Detection & Prevention Market Outlook, By Identity Theft Detection (2024-2032) ($MN)
  • Table 22 Global AI for Fraud Detection & Prevention Market Outlook, By Account Takeover Prevention (2024-2032) ($MN)
  • Table 23 Global AI for Fraud Detection & Prevention Market Outlook, By Payment Fraud Detection (2024-2032) ($MN)
  • Table 24 Global AI for Fraud Detection & Prevention Market Outlook, By Insurance Claim Fraud (2024-2032) ($MN)
  • Table 25 Global AI for Fraud Detection & Prevention Market Outlook, By Anti-Money Laundering (AML) (2024-2032) ($MN)
  • Table 26 Global AI for Fraud Detection & Prevention Market Outlook, By Behavioral Biometrics (2024-2032) ($MN)
  • Table 27 Global AI for Fraud Detection & Prevention Market Outlook, By Synthetic Identity Detection (2024-2032) ($MN)
  • Table 28 Global AI for Fraud Detection & Prevention Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 29 Global AI for Fraud Detection & Prevention Market Outlook, By End User (2024-2032) ($MN)
  • Table 30 Global AI for Fraud Detection & Prevention Market Outlook, By Banking, Financial Services, and Insurance (BFSI) (2024-2032) ($MN)
  • Table 31 Global AI for Fraud Detection & Prevention Market Outlook, By Government and Public Sector (2024-2032) ($MN)
  • Table 32 Global AI for Fraud Detection & Prevention Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 33 Global AI for Fraud Detection & Prevention Market Outlook, By IT and Telecommunications (2024-2032) ($MN)
  • Table 34 Global AI for Fraud Detection & Prevention Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 35 Global AI for Fraud Detection & Prevention Market Outlook, By Energy (2024-2032) ($MN)
  • Table 36 Global AI for Fraud Detection & Prevention Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.