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市場調查報告書
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2058818

人工智慧驅動的詐欺偵測和風險分析平台市場預測至2034年：按組件、平台類型、詐欺類型、技術、應用、最終用戶和地區分類的全球分析

AI-Powered Fraud Detection & Risk Analytics Platforms Market Forecasts to 2034 - Global Analysis By Component (Solutions and Services), Platform Type, Fraud Type, Technology, Application, 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 的數據，全球人工智慧驅動的詐欺偵測和風險分析平台市場預計將在 2026 年達到 350 億美元，在預測期內以 17.8% 的複合年成長率成長，到 2034 年達到 1,294 億美元。

人工智慧驅動的詐欺偵測和風險分析平台利用先進的演算法、機器學習和資料建模技術，即時識別可疑活動並評估潛在風險。這些系統分析大量的交易和行為數據，以偵測異常情況、預測詐欺模式並提高決策準確性。透過不斷從新數據中學習，它們增強了檢測能力，減少了誤報，並幫助企業在數位和傳統金融環境中加強安全、確保合規性並最大限度地減少財務損失。

數位交易量的激增增加了詐欺風險。

數位支付、電子商務、行動銀行和加密貨幣交易的快速發展，為詐欺活動創造了一個日益複雜且規模龐大的環境。網路犯罪分子正利用數位金融系統中的漏洞，採用合成身分詐騙、帳戶劫持和人工智慧產生的深度造假攻擊等複雜技術進行犯案。傳統的基於規則的詐欺偵測系統已無法應對現代金融犯罪的速度、規模和不斷湧現的新模式。這種威脅的加劇迫使金融機構、零售商和支付處理商大力投資人工智慧驅動的詐欺偵測平台，以便即時、自適應地識別和應對威脅。

誤報率過高會損害客戶體驗和營運效率。

儘管技術取得了顯著進步，但人工智慧驅動的詐欺偵測系統仍面臨著較高的誤報率，會將合法交易錯誤地標記為詐欺交易。這會給客戶體驗帶來不便，尤其是在交易核准速度至關重要的高頻零售支付場景中。誤報可能導致交易被拒絕、帳戶被凍結、客戶服務成本增加，甚至可能將客戶推向競爭對手的平台。如何在提高詐欺偵測靈敏度和使用者體驗品質之間取得平衡仍然是一個複雜的最佳化難題，需要持續的模型重訓練、大量的標註訓練資料以及針對不同交易場景的特定領域調整。

行為生物特徵識別與連續認證模型的整合

將動態擊鍵特徵、裝置操作模式和位置​​資料分析等行為生物辨識技術整合到詐欺偵測平台中，蘊藏著巨大的市場機會。與靜態身份驗證方法不同，行為生物識別技術能夠在使用者會話期間進行持續被動的風險評估，即時檢測表明帳戶被盜用或會話劫持的異常情況。採用這些功能的金融機構將受益於詐欺偵測率的顯著提升，同時減少對傳統高階身分驗證方式的依賴。隨著行為資料調查方法日趨成熟並符合隱私法規，預計銀行、保險和支付生態系統中持續身分驗證的普及速度將顯著加快。

對抗性人工智慧攻擊旨在規避偵測演算法

網路犯罪分子正日益精進其規避人工智慧詐欺偵測系統的手段，他們利用對抗性機器學習技術，對市場構成根本性的嚴重威脅。攻擊者可以透過反覆進行小額交易來分析詐欺偵測模型的行為模式，然後調整其後續的詐欺活動，使其低於偵測閾值。生成式人工智慧進一步增強了犯罪分子創建高度逼真的合成身份、深度造假身份證明文件以及人工智慧生成的釣魚郵件的能力。為了應對這場對抗性軍備競賽並維持有效的詐欺防範，必須持續投資於模型可解釋性、對抗穩健性測試和整合偵測調查方法。

新冠疫情的感染疾病：

新冠疫情導致數位金融詐騙激增。這是因為數百萬消費者首次轉向網路銀行和電子商務，造成大量缺乏經驗的數位使用者容易受到網路釣魚和社交工程攻擊。同時，疫情造成的經濟困境也助長了第一方詐騙的增加，包括詐欺性貸款申請和保險索賠。在此期間，那些在人工智慧驅動的防詐欺基礎設施方面投資不足的金融機構遭受了不成比例的損失，這促使它們在後疫情時代加速投資於先進的檢測平台。這場危機永久提高了人們對詐欺風險的認知，並促使政府持續增加對人工智慧驅動的金融犯罪預防的預算投入。

在預測期內，解決方案領域預計將佔據最大的市場佔有率。

在預測期內，解決方案領域預計將佔據最大的市場佔有率。這是因為涵蓋交易監控、異常檢測、身份驗證和即時決策引擎的核心技術平台構成了生態系統的主要價值創造層。金融機構和企業正優先投資於解決方案基礎設施，以應對與詐欺損失相關的直接財務和聲譽風險。人工智慧能力的持續發展，例如將圖分析和自然語言處理整合到詐欺檢測平台中，正在推動各行業對解決方案採購和授權的強勁需求。

在預測期內，服務業預計將呈現最高的複合年成長率。

在預測期內，服務板塊預計將呈現最高成長率，這主要得益於對詐欺分析諮詢、平台整合、模型訓練和託管檢測服務需求的不斷成長。隨著詐欺模式的快速演變和監管合規要求的日益嚴格，各機構越來越依賴專業服務供應商來最佳化其人工智慧詐欺檢測模型、開展紅隊演練並維持營運檢測的準確性。隨著多通路詐騙手段日益複雜，需要跨平台資料整合，對專家部署和持續管理服務的需求也日益成長，尤其是在缺乏內部人工智慧詐騙專業知識的中型金融機構中。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於該地區龐大的數位支付交易量、先進的金融服務業以及成熟的網路安全投資體系。美國在全球金融詐騙損失中佔據相當大的比例，為其採用先進平台提供了強大的製度獎勵。消費者金融保護局 (CFPB) 和金融犯罪執法網路 (FinCEN) 等監管機構的要求進一步強化了健全的詐欺防制和反洗錢 (AML) 管理系統。此外，眾多領先的人工智慧詐欺偵測供應商總部設在北美，進一步鞏固了它們在該地區市場的主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度、東南亞和澳洲數位支付、行動銀行和電子商務的快速發展。印度統一支付介面（UPI）生態系統以及中國支付寶和微信支付網路等市場中龐大的即時支付交易量，對詐騙基礎設施的需求也顯著成長。針對區域金融機構的網路犯罪手段日益複雜，加之監管機構對銀行在反洗錢和反詐欺能力方面投入的日益成長的壓力，正在加速人工智慧平台在全部區域的應用。

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目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 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
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章 全球人工智慧驅動的詐欺偵測與風險分析平台市場：按組件分類

  • 解決方案
  • 服務
    • 諮詢
    • 整合和部署
    • 支援與維護
    • 託管服務

第6章：全球人工智慧驅動的詐欺偵測與風險分析平台市場：按平台類型分類

  • 交易詐欺偵測平台
  • AI行為詐欺偵測平台
  • AI支付詐欺偵測平台
  • 金融詐欺情報平台
  • 人工智慧風險分析平台
  • 即時詐欺情報平台
  • 人工智慧金融犯罪檢測系統

第7章 全球人工智慧驅動的詐欺偵測與風險分析平台市場：按詐欺類型分類

  • 支付詐欺
  • 身分盜竊/身分詐騙
  • 帳戶劫持詐騙
  • 信貸和貸款詐騙
  • 保險詐欺
  • 洗錢/反洗錢詐騙
  • 其他類型的詐欺

第8章 全球人工智慧驅動的詐欺偵測和風險分析平台市場：按技術分類

  • 機器學習（ML）
  • 深度學習
  • 自然語言處理（NLP）
  • 預測分析
  • 行為分析
  • 圖表分析
  • 生物識別

第9章 全球人工智慧驅動的詐欺偵測與風險分析平台市場：按應用分類

  • 銀行詐欺偵測
  • 支付詐欺監控
  • 保險詐欺偵測
  • 電子商務詐欺預防
  • 財務風險分析
  • 反洗錢措施
  • 防止個人資訊盜竊
  • 合規性監控

第10章 全球人工智慧驅動的詐欺偵測和風險分析平台市場：按最終用戶分類

  • 銀行業、金融服務業及保險業
  • 零售與電子商務
  • 衛生保健
  • 政府/公共部門
  • 資訊科技/通訊
  • 能源公用事業
  • 製造業

第11章 全球人工智慧驅動的詐欺偵測與風險分析平台市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • International Business Machines Corporation
  • SAS Institute Inc.
  • FICO
  • Oracle Corporation
  • Experian plc
  • ACI Worldwide
  • Feedzai
  • Riskified
  • Kount
  • Forter
  • Stripe
  • PayPal
  • Mastercard
  • SEON Technologies
  • Veriff
Product Code: SMRC36422

According to Stratistics MRC, the Global AI-Powered Fraud Detection & Risk Analytics Platforms Market is accounted for $35.0 billion in 2026 and is expected to reach $129.4 billion by 2034 growing at a CAGR of 17.8% during the forecast period. AI-powered fraud detection and risk analytics platforms use advanced algorithms, machine learning, and data modeling techniques to identify suspicious activities and assess potential risks in real time. These systems analyze large volumes of transactional and behavioral data to detect anomalies, predict fraud patterns, and enhance decision-making accuracy. By continuously learning from new data, they improve detection capabilities, reduce false positives, and help organizations strengthen security, ensure regulatory compliance, and minimize financial losses across digital and traditional financial environments.

Market Dynamics:

Driver:

Exponential growth in digital transaction volumes amplifying fraud exposure

The rapid expansion of digital payments, e-commerce, mobile banking, and cryptocurrency transactions is creating an increasingly complex and high-volume environment for fraud perpetration. Cybercriminals are leveraging sophisticated techniques including synthetic identity fraud, account takeover, and AI-generated deepfake attacks to exploit vulnerabilities in digital financial systems. Traditional rule-based fraud detection systems are unable to keep pace with the speed, volume, and novel patterns of modern financial crime. This escalating threat landscape is compelling financial institutions, retailers, and payment processors to invest heavily in AI-powered fraud detection platforms capable of real-time adaptive threat identification and response.

Restraint:

High false positive rates undermining customer experience and operational efficiency

Despite significant technological advances, AI-powered fraud detection systems continue to generate elevated false positive rates, incorrectly flagging legitimate transactions as fraudulent. This creates friction in customer journeys, particularly in high-frequency retail payment scenarios where transaction approval speed is critical. False positives result in declined transactions, account suspensions, and increased customer service costs, potentially driving customers toward competitor platforms. Balancing fraud detection sensitivity with user experience quality remains a complex optimization challenge that requires continuous model retraining, extensive labeled training data, and domain-specific calibration across diverse transaction contexts.

Opportunity:

Integration of behavioral biometrics and continuous authentication models

The integration of behavioral biometrics including keystroke dynamics, device interaction patterns, and geolocation analytics into fraud detection platforms represents a significant market opportunity. Unlike static authentication methods, behavioral biometrics enable continuous, passive risk assessment throughout an entire user session, detecting anomalies indicative of account takeover or session hijacking in real time. Financial institutions deploying these capabilities benefit from reduced reliance on disruptive step-up authentication while substantially improving fraud catch rates. As behavioral data collection methodologies become more sophisticated and privacy-compliant, adoption of continuous authentication across banking, insurance, and payment ecosystems is expected to accelerate markedly.

Threat:

Adversarial AI attacks designed to evade detection algorithms

The growing sophistication of cybercriminals who leverage adversarial machine learning techniques to probe, understand, and systematically evade AI fraud detection systems represents a fundamental and escalating threat to the market. By analyzing the behavioral patterns of fraud detection models through repeated low-value transactions, attackers can calibrate subsequent fraudulent activities to fall below detection thresholds. Generative AI is further empowering criminals to create highly convincing synthetic identities, deepfake verification materials, and AI-crafted phishing communications. This adversarial arms race demands continuous investment in model explainability, adversarial robustness testing, and ensemble detection methodologies to maintain effective fraud prevention.

Covid-19 Impact:

The COVID-19 pandemic triggered a significant surge in digital financial fraud as millions of consumers shifted to online banking and e-commerce for the first time, creating a large population of inexperienced digital users susceptible to phishing and social engineering attacks. Simultaneously, the economic hardship generated by the pandemic incentivized a rise in first-party fraud, including fraudulent loan applications and insurance claims. Financial institutions that had underinvested in AI fraud infrastructure faced disproportionate losses during this period, accelerating post-pandemic investment in advanced detection platforms. The crisis permanently elevated awareness of fraud risk and drove sustained budget allocation toward AI-powered financial crime prevention.

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

The solutions segment is expected to account for the largest market share during the forecast period, as the core technology platforms encompassing transaction monitoring, anomaly detection, identity verification, and real-time decisioning engines represent the primary value creation layer of the ecosystem. Financial institutions and enterprises prioritize investment in solution infrastructure to address the direct financial and reputational risks associated with fraud losses. The continuous evolution of AI capabilities, including the integration of graph analytics and natural language processing into fraud platforms, sustains strong and growing demand for solution procurement and licensing across all industry verticals.

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

Over the forecast period, the services segment is predicted to witness the highest growth rate, driven by escalating demand for fraud analytics consulting, platform integration, model training, and managed detection services. As fraud patterns evolve rapidly and regulatory compliance requirements intensify, organizations increasingly rely on specialized service providers to optimize their AI fraud models, conduct red team exercises, and maintain operational detection accuracy. The growing complexity of multi-channel fraud schemes requiring cross-platform data integration further amplifies demand for expert deployment and ongoing management services, particularly among mid-market financial institutions lacking in-house AI fraud expertise.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the region's high digital payment transaction volumes, sophisticated financial services sector, and mature cybersecurity investment culture. The United States accounts for a significant proportion of global financial fraud losses, creating strong institutional incentives for advanced platform adoption. Regulatory requirements from bodies such as the Consumer Financial Protection Bureau and the Financial Crimes Enforcement Network further mandate robust fraud and AML controls. The presence of leading AI fraud detection vendors headquartered in North America reinforces the region's dominant market position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, propelled by the rapid expansion of digital payments, mobile banking, and e-commerce across China, India, Southeast Asia, and Australia. The high volume of real-time payment transactions in markets such as India's UPI ecosystem and China's Alipay and WeChat Pay networks creates substantial fraud detection infrastructure requirements. Rising cybercrime sophistication targeting regional financial institutions, combined with increasing regulatory pressure on banks to invest in AML and fraud prevention capabilities, is driving accelerated AI platform adoption across the region.

Key players in the market

Some of the key players in AI-Powered Fraud Detection & Risk Analytics Platforms Market include International Business Machines Corporation, SAS Institute Inc., FICO, Oracle Corporation, Experian plc, ACI Worldwide, Feedzai, Riskified, Kount, Forter, Stripe, PayPal, Mastercard, SEON Technologies, and Veriff.

Key Developments:

In April 2026, FICO unveiled a next-generation fraud detection platform incorporating large language model capabilities to analyze unstructured transaction metadata and customer communication patterns, enabling financial institutions to detect complex fraud typologies including social engineering scams with significantly improved accuracy.

In February 2026, Feedzai completed the acquisition of a European behavioral analytics firm, integrating advanced device fingerprinting and session behavioral intelligence into its risk management platform to enhance real-time account takeover detection across mobile and web banking channels.

Components Covered:

  • Solutions
  • Services

Platform Types Covered:

  • Transaction Fraud Detection Platforms
  • AI Behavioral Fraud Detection Platforms
  • AI Payment Fraud Platforms
  • Financial Fraud Intelligence Platforms
  • AI Risk Analytics Platforms
  • Real-Time Fraud Intelligence Platforms
  • AI Financial Crime Detection Systems

Fraud Types Covered:

  • Payment Fraud
  • Identity Theft / Identity Fraud
  • Account Takeover Fraud
  • Credit & Lending Fraud
  • Insurance Fraud
  • Money Laundering / AML Fraud
  • Other Fraud Types

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Predictive Analytics
  • Behavioral Analytics
  • Graph Analytics
  • Biometric Authentication

Applications Covered:

  • Banking Fraud Detection
  • Payment Fraud Monitoring
  • Insurance Fraud Detection
  • E-commerce Fraud Prevention
  • Financial Risk Analytics
  • Anti-Money Laundering (AML)
  • Identity Theft Protection
  • Compliance Monitoring

End Users Covered:

  • Banking, Financial Services & Insurance
  • Retail & E-commerce
  • Healthcare
  • Government & Public Sector
  • IT & Telecom
  • Energy & Utilities
  • Manufacturing

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By Component

  • 5.1 Solutions
  • 5.2 Services
    • 5.2.1 Consulting
    • 5.2.2 Integration & Implementation
    • 5.2.3 Support & Maintenance
    • 5.2.4 Managed Services

6 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By Platform Type

  • 6.1 Transaction Fraud Detection Platforms
  • 6.2 AI Behavioral Fraud Detection Platforms
  • 6.3 AI Payment Fraud Platforms
  • 6.4 Financial Fraud Intelligence Platforms
  • 6.5 AI Risk Analytics Platforms
  • 6.6 Real-Time Fraud Intelligence Platforms
  • 6.7 AI Financial Crime Detection Systems

7 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By Fraud Type

  • 7.1 Payment Fraud
  • 7.2 Identity Theft / Identity Fraud
  • 7.3 Account Takeover Fraud
  • 7.4 Credit & Lending Fraud
  • 7.5 Insurance Fraud
  • 7.6 Money Laundering / AML Fraud
  • 7.7 Other Fraud Types

8 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By Technology

  • 8.1 Machine Learning (ML)
  • 8.2 Deep Learning
  • 8.3 Natural Language Processing (NLP)
  • 8.4 Predictive Analytics
  • 8.5 Behavioral Analytics
  • 8.6 Graph Analytics
  • 8.7 Biometric Authentication

9 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By Application

  • 9.1 Banking Fraud Detection
  • 9.2 Payment Fraud Monitoring
  • 9.3 Insurance Fraud Detection
  • 9.4 E-commerce Fraud Prevention
  • 9.5 Financial Risk Analytics
  • 9.6 Anti-Money Laundering (AML)
  • 9.7 Identity Theft Protection
  • 9.8 Compliance Monitoring

10 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By End User

  • 10.1 Banking, Financial Services & Insurance
  • 10.2 Retail & E-commerce
  • 10.3 Healthcare
  • 10.4 Government & Public Sector
  • 10.5 IT & Telecom
  • 10.6 Energy & Utilities
  • 10.7 Manufacturing

11 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 International Business Machines Corporation
  • 14.2 SAS Institute Inc.
  • 14.3 FICO
  • 14.4 Oracle Corporation
  • 14.5 Experian plc
  • 14.6 ACI Worldwide
  • 14.7 Feedzai
  • 14.8 Riskified
  • 14.9 Kount
  • 14.10 Forter
  • 14.11 Stripe
  • 14.12 PayPal
  • 14.13 Mastercard
  • 14.14 SEON Technologies
  • 14.15 Veriff

List of Tables

  • Table 1 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Solutions (2023-2034) ($MN)
  • Table 4 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Consulting (2023-2034) ($MN)
  • Table 6 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Integration & Implementation (2023-2034) ($MN)
  • Table 7 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Support & Maintenance (2023-2034) ($MN)
  • Table 8 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 9 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Platform Type (2023-2034) ($MN)
  • Table 10 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Transaction Fraud Detection Platforms (2023-2034) ($MN)
  • Table 11 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By AI Behavioral Fraud Detection Platforms (2023-2034) ($MN)
  • Table 12 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By AI Payment Fraud Platforms (2023-2034) ($MN)
  • Table 13 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Financial Fraud Intelligence Platforms (2023-2034) ($MN)
  • Table 14 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By AI Risk Analytics Platforms (2023-2034) ($MN)
  • Table 15 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Real-Time Fraud Intelligence Platforms (2023-2034) ($MN)
  • Table 16 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By AI Financial Crime Detection Systems (2023-2034) ($MN)
  • Table 17 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Fraud Type (2023-2034) ($MN)
  • Table 18 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Payment Fraud (2023-2034) ($MN)
  • Table 19 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Identity Theft / Identity Fraud (2023-2034) ($MN)
  • Table 20 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Account Takeover Fraud (2023-2034) ($MN)
  • Table 21 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Credit & Lending Fraud (2023-2034) ($MN)
  • Table 22 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Insurance Fraud (2023-2034) ($MN)
  • Table 23 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Money Laundering / AML Fraud (2023-2034) ($MN)
  • Table 24 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Other Fraud Types (2023-2034) ($MN)
  • Table 25 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Technology (2023-2034) ($MN)
  • Table 26 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 27 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 28 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 29 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 30 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Behavioral Analytics (2023-2034) ($MN)
  • Table 31 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Graph Analytics (2023-2034) ($MN)
  • Table 32 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Biometric Authentication (2023-2034) ($MN)
  • Table 33 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Application (2023-2034) ($MN)
  • Table 34 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Banking Fraud Detection (2023-2034) ($MN)
  • Table 35 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Payment Fraud Monitoring (2023-2034) ($MN)
  • Table 36 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Insurance Fraud Detection (2023-2034) ($MN)
  • Table 37 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By E-commerce Fraud Prevention (2023-2034) ($MN)
  • Table 38 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Financial Risk Analytics (2023-2034) ($MN)
  • Table 39 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Anti-Money Laundering (AML) (2023-2034) ($MN)
  • Table 40 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Identity Theft Protection (2023-2034) ($MN)
  • Table 41 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Compliance Monitoring (2023-2034) ($MN)
  • Table 42 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By End User (2023-2034) ($MN)
  • Table 43 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Banking, Financial Services & Insurance (2023-2034) ($MN)
  • Table 44 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
  • Table 45 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 46 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Government & Public Sector (2023-2034) ($MN)
  • Table 47 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By IT & Telecom (2023-2034) ($MN)
  • Table 48 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Energy & Utilities (2023-2034) ($MN)
  • Table 49 Global AI-Powered Fraud Detection & Risk Analytics Platforms Market Outlook, By Manufacturing (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.