查看購物車
  • Simplified Chinese
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1798085

2032 年人工智慧詐欺偵測與預防市場預測：按組件、部署類型、組織規模、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球用於詐欺偵測和預防的人工智慧市場預計將在 2025 年達到 149.1 億美元，到 2032 年將達到 536.2 億美元，預測期內的複合年成長率為 20.06%。

用於詐欺檢測和預防的人工智慧利用數據分析和先進的機器學習演算法，即時發現可疑活動、趨勢和異常。透過分析大量交易、行為和歷史數據，人工智慧系統能夠比傳統方法更快、更準確地識別潛在詐欺行為。透過異常檢測、預測建模和自然語言處理等技術，網路安全團隊、電商平台和金融機構可以改善決策，減少誤報，並預測詐欺活動。由於人工智慧能夠持續從新數據中學習，隨著詐騙手段日益複雜，詐騙防制也變得更加主動、靈活和有效。

根據 BioCatch 行為生物識別協會的數據，74% 的金融機構已經在使用人工智慧來檢測金融犯罪，73% 的金融機構正在使用人工智慧來檢測欺詐，這表明人工智慧主導的安全框架得到了廣泛的採用和組織信任。

日益成長的網路威脅和先進的詐騙手段

網路威脅日益複雜，包括深度造假、網路釣魚、冒充和合成詐騙，這推動了對更智慧安全解決方案的需求。傳統的基於規則的系統無法識別細微且不斷演變的詐欺模式，常常導致重大的財務和聲譽損失。人工智慧主導的平台使用行為分析、異常檢測和機器學習來持續分析大型資料集並適應新出現的威脅。透過即時檢測異常行為並從歷史模式中學習，人工智慧可以降低風險敞口並實現主動干預。此外，隨著詐騙變得越來越老練，人工智慧的預測能力對於保護通訊、電子商務和金融服務領域的數位生態系統至關重要。

實施和維護成本高

部署人工智慧詐欺偵測系統需要在軟體、硬體和專業人才方面進行大量的初始投資。人工智慧平台必須與組織現有的IT基礎設施頻繁整合，這既具有挑戰性，又成本高昂。此外，維護此類系統需要持續監控、更新和重新訓練人工智慧模型，以適應詐騙策略的變化。這些成本可能會限制其採用，對於中小型企業來說，成本過高。儘管人工智慧的優勢顯而易見，但高昂的成本會延遲其採用，降低投資收益，並阻礙一些公司全面採用人工智慧詐欺預防技術。

電子商務和數位支付的使用日益增多

在全球範圍內，數位銀行、行動錢包和網路購物的快速發展導致數位交易量激增。由於傳統方法無法應對高頻、多通路交易，這種擴張為基於人工智慧的詐欺偵測系統提供了巨大的機會。人工智慧可以即時分析大量數據，並在詐欺、異常模式和潛在詐騙影響客戶和企業之前識別它們。為了維護消費者信任並最大限度地減少財務損失，電子商務平台、金融科技Start-Ups和數位支付提供商正在增加對人工智慧的投資。隨著數位交易的持續成長，對強大的人工智慧詐欺預防解決方案的需求預計將快速成長。

解決方案提供者之間的激烈競爭

人工智慧詐欺偵測市場競爭日益激烈，許多國內外供應商提供的解決方案千差萬別。為了在激烈的競爭中吸引並留住客戶，企業不斷面臨創新、降低價格和提升服務品質的壓力。規模較小的公司難以與擁有更豐富資源和更成熟技術的老牌供應商競爭，而新參與企業則可能難以建立信譽和信任。此外，這種競爭環境可能會減緩整體市場成長，增加行銷和研發費用，並降低利潤率。為了維持市場佔有率並維持長期成長，企業必須透過尖端技術、一流的客戶服務和策略合作夥伴關係來脫穎而出。

COVID-19的影響

新冠疫情大大加速了眾多產業的數位轉型，導致線上交易、遠距銀行、電子商務和數位支付激增，也增加了詐騙的可能性。由於傳統方法無法應對線上交易的數量和複雜性，這種快速變化推動了對基於人工智慧的詐欺檢測和預防解決方案的需求。為了確保業務連續性和客戶信任，企業迅速採用人工智慧技術來即時監控、分析和回應可疑活動。此外，疫情凸顯了對雲端基礎的可擴展人工智慧系統的需求，這些系統能夠適應新的詐欺趨勢和快速發展的數位行為。

預計雲端運算市場將成為預測期內最大的市場

預計雲端技術將在預測期內佔據最大的市場佔有率。這得益於雲端解決方案的可擴展性、成本效益和靈活性，使企業能夠快速適應不斷變化的詐欺策略。雲端基礎的平台支援即時數據處理和多通路整合，進而提升詐欺偵測和預防能力。此外，由雲端的集中式基礎設施支援的先進人工智慧模型、機器學習演算法和行為分析對於發現複雜的詐欺趨勢至關重要。這些特性使雲端技術成為希望在不犧牲營運靈活性的情況下改善詐欺偵測系統的企業的理想選擇。

預計機器學習領域在預測期內將以最高複合年成長率成長

預計機器學習領域將在預測期內實現最高成長率。機器學習可實現即時詐欺偵測，廣泛應用於在海量資料集中查找模式和異常。機器學習 (ML) 系統利用不斷從交易和歷史資料中學習的演算法，從而能夠預測和預防欺詐，並隨著時間的推移提高準確性。該領域因其在銀行、電子商務、保險和通訊等領域的多功能性而引領市場。此外，機器學習能夠最大限度地減少誤報、自動化詐欺偵測程序並提高決策效率，使其成為現代反詐欺解決方案的重要組成部分。

比最大的地區

預計北美地區將在預測期內佔據最大的市場佔有率。該地區較高的數位支付滲透率、先進的技術基礎設施以及IBM、微軟和甲骨文等主要參與者的存在，正在推動欺詐檢測解決方案領域的競爭和創新，是其主導的關鍵因素。數位交易的興起和網路威脅的日益複雜，尤其使美國處於領先地位。此外，機器學習和深度學習等人工智慧技術的整合顯著提升了詐欺偵測系統的能力，使北美目前在該領域處於領先地位。

複合年成長率最高的地區

預計亞太地區在預測期內的複合年成長率最高。中國、印度、日本、澳洲和東南亞國家等主要經濟體的快速數位轉型是這一強勁成長的主要驅動力。數位錢包、電子商務、網路銀行和行動支付系統的快速普及，推動了數位交易生態系統的快速發展。此外，詐欺和網路攻擊的風險也不斷增加。為了保護業務和客戶訊息，該領域的公司擴大採用基於人工智慧的詐欺檢測系統。

免費客製化服務

訂閱此報告的客戶可享有以下免費自訂選項之一：

  • 公司簡介
    • 對最多三家其他市場參與企業進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 透過產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 二手研究資料
    • 先決條件

第3章市場走勢分析

  • 介紹
  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

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. 全球人工智慧詐欺偵測與預防市場（按組件）

  • 介紹
  • 解決方案
  • 服務

6. 全球詐欺偵測與預防人工智慧市場（按部署類型）

  • 介紹
  • 雲
  • 本地
  • 混合

第7章詐騙偵測與預防人工智慧市場（依組織規模）

  • 介紹
  • 小型企業
  • 主要企業

第 8 章 人工智慧詐騙偵測與預防市場（按技術）

  • 介紹
  • 機器學習
  • 深度學習
  • 自然語言處理
  • 圖形分析
  • 聯邦學習和隱私保護人工智慧
  • 其他

9. 全球人工智慧詐欺偵測與預防市場（按應用）

  • 介紹
  • 交易監控
  • 身份盜竊偵測
  • 帳戶盜用預防
  • 支付詐騙檢測
  • 保險詐騙
  • 反洗錢（AML）
  • 行為生物識別
  • 合成身份檢測
  • 其他

10. 全球詐欺偵測與預防人工智慧市場（按最終用戶）

  • 介紹
  • 銀行、金融服務和保險（BFSI）
  • 政府和公共部門
  • 衛生保健
  • 資訊科技和通訊
  • 製造業
  • 能源
  • 其他

11. 全球人工智慧詐欺偵測與預防市場（按地區）

  • 介紹
  • 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
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 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
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第13章：公司概況

  • 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.