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1880430

人工智慧驅動的詐欺預測網路市場預測至2032年：按組件、部署類型、應用、最終用戶和地區分類的全球分析

AI-Powered Fraud-Prediction Networks Market Forecasts to 2032 - Global Analysis By Component, Deployment, 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 的一項研究，全球人工智慧驅動的詐欺預測網路市場預計到 2025 年將達到 108 億美元，到 2032 年將達到 386 億美元，在預測期內以 20% 的複合年成長率成長。

人工智慧驅動的詐欺預測網路利用機器學習和人工智慧技術分析大量交易資料集，即時偵測模式並識別異常情況，從而發現詐欺活動。這些自適應系統不斷學習新的詐欺手段，最大限度地減少誤報，並自動發出警報，從而提高安全性，減少經濟損失，並增強銀行、電子商務、身份驗證和保險等行業的信任度。

根據國際清算銀行（BIS）的說法，分析多家銀行交易模式的聯盟人工智慧模型在檢測複雜的跨機構支付詐騙要有效得多。

即時交易詐騙呈上升趨勢。

即時交易詐騙的日益猖獗，推動了企業對能夠以毫秒級延遲檢測細微異常的自適應、原生人工智慧預測網路的需求。數位支付、跨境電商和即時支付系統的普及，促使金融機構將主動防範詐欺置於被動調查之上。攻擊手段的日益複雜化，尤其是針對行動錢包和嵌入式金融平台的攻擊，正在加速平台現代化進程。因此，供應商正在擴展其基於圖的推理引擎，以在不斷演變的威脅環境中提升情境決策能力並減少誤報。

由於詐欺特徵快速變化，導致模型漂移較大

由於攻擊者不斷改變其行為模式以逃避檢測，快速變化的詐欺特徵導致模型漂移嚴重，這仍然是一個重大障礙。由於交易流程高度多變且欺詐手段具有地域性，監督式模型若不頻繁重新訓練，性能往往會下降，從而給營運帶來沉重負擔。這種漂移需要持續的特徵工程、高品質的標註和管道調整，推高了銀行和金融科技公司的成本結構。因此，許多機構難以維持可靠的預測效能，尤其是在詐欺量出現不可預測的激增時。

行為生物辨識技術的融合

行為生物特徵技術的融合為擴展詐騙預測網路提供了一條強力的途徑，使其超越靜態憑證，能夠評估意圖驅動的、細緻入微的互動行為。在身分盜竊和合成身分詐騙猖獗的推動下，金融機構正將擊鍵動態、步態模式、觸控螢幕壓力和互動節奏等資訊整合到多模態詐騙評分引擎中。這種融合增強了持續身份驗證，並改善了高速數位管道中的風險細分。因此，下一代人工智慧風險平台能夠在不影響客戶便利性的前提下，更準確地偵測異常情況，區分合法使用者和有組織的詐騙行為。

對抗性人工智慧會降低預測準確性

對抗性人工智慧正在削弱預測準確率，構成重大威脅，因為惡意攻擊者會利用生成模型來建構模仿合法使用者行為的攻擊模式。在自動化「詐騙即服務」生態系統的推動下，這些對抗性代理會利用模型盲點，降低分類器的可靠性並增加漏報率。此外，針對訓練資料集的定向投毒會破壞詐騙防制流程。這場不斷升級的軍備競賽迫使供應商採用強大的模型加固、持續的對抗性測試和容錯整合架構來維持防禦效力。

新冠疫情的影響：

感染疾病加速了支付數位化，卻也無意間導致了網路釣魚、帳號盜用和紓困金詐騙等犯罪活動的空前激增。隨著遠端開戶和非接觸式交易的普及，金融機構部署了人工智慧驅動的詐欺預測工具，以應對日益成長的營運風險。消費者脆弱性的增加和麵對面身份驗證的減少，加速了對自動化風險評分引擎和行為監控模組的需求。即使在疫情結束後，詐欺預測網路對於保障數位管道的安全仍然至關重要，因此，可擴展的雲端原生分析和持續身分驗證框架的投資仍在持續。

預計在預測期內，詐欺偵測引擎細分市場將佔據最大的市場佔有率。

由於詐欺偵測引擎在高速支付環境中即時異常評分方面發揮核心作用，預計在預測期內，詐欺偵測引擎細分市場將佔據最大的市場佔有率。在對基於深度學習的模式識別日益成長的需求驅動下，這些引擎聚合交易數據、設備資訊和行為遙測數據，從而大規模產生風險訊號。它們在銀行、保險和電子商務生態系統中的廣泛應用進一步鞏固了其市場主導地位。此外，圖分析和自適應規則編配的快速發展也進一步強化了主導地位。

預計在預測期內，雲端基礎的系統細分市場將呈現最高的複合年成長率。

在預測期內，雲端基礎的系統領域預計將實現最高成長率，這主要得益於各機構從傳統的本地風險引擎遷移到擴充性的、API驅動的詐欺偵測智慧平台。憑藉即時交易量和加速的全球支付流程，雲端架構能夠實現快速模型部署、持續更新以及跨區域威脅遙測資料共用。計量收費的經濟模式以及與數位銀行系統的無縫整合將進一步推動雲端架構的普及。這種靈活性對於需要即時詐欺應變能力的金融科技公司和新型銀行而言尤其重要。

佔比最大的地區：

由於數位錢包、QR碼支付和超級應用生態系統的爆炸性成長，亞太地區預計將在預測期內佔據最大的市場佔有率。在高行動普及率和不斷成長的跨境匯款流量的推動下，該地區的詐騙風險日益上升，促使各方對人工智慧驅動的風險評分框架進行大量投資。此外，印度、新加坡和澳洲的監管機構正在強制要求更嚴格的身份驗證和詐騙監控控制措施。這些趨勢使亞太地區成為即時詐騙預測網路應用最廣泛的地區。

年複合成長率最高的地區：

預計在預測期內，北美將實現最高的複合年成長率，這主要得益於銀行、卡組織和數位化優先貸款機構對先進詐欺檢測平台的快速採用。日益複雜的網路犯罪以及消費者保護日益嚴格的監管審查，正在加速系統升級。此外，該地區匯聚了許多領先的人工智慧風險分析供應商，從而加快了對抗性檢測、行為生物識別和聯邦學習等領域的創新週期。不斷發展的金融科技生態系統和即時支付基礎設施進一步提升了對可擴展的雲端原生詐欺預測網路的需求。

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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
    • 原始研究資料
    • 次級研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的影響

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

第5章 全球人工智慧驅動的詐欺預測網路市場（按組件分類）

  • 介紹
  • 詐騙偵測引擎
  • 行為分析模組
  • 身份驗證系統
  • 交易監控平台
  • 風險評分模型

第6章 全球基於人工智慧的詐欺預測網路市場（按實施類型分類）

  • 介紹
  • 雲端基礎的系統
  • 本地部署平台
  • 混合基礎設施
  • 邊緣人工智慧詐騙偵測節點
  • 分散式詐騙網路

7. 全球基於人工智慧的詐欺預測網路市場（按應用分類）

  • 介紹
  • 金融服務業欺詐管理
  • 電子商務交易安全
  • 身份和存取詐騙
  • 付款閘道監控
  • 數位錢包安全

第8章 全球人工智慧驅動的詐欺預測網路市場（按最終用戶分類）

  • 介紹
  • 銀行和非銀行金融公司
  • 電子商務企業
  • 金融科技公司
  • 通訊業者
  • 保險公司

9. 全球人工智慧驅動的詐欺預測網路市場（按地區分類）

  • 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
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 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10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與併購
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • FICO
  • Experian
  • NICE Actimize
  • SAS
  • LexisNexis Risk Solutions
  • Featurespace
  • Forter
  • Sift
  • Kount
  • Darktrace
  • DataVisor
  • Mastercard
  • Visa
  • PayPal
  • Feedzai
  • ACI Worldwide
Product Code: SMRC32475

According to Stratistics MRC, the Global AI-Powered Fraud-Prediction Networks Market is accounted for $10.8 billion in 2025 and is expected to reach $38.6 billion by 2032 growing at a CAGR of 20% during the forecast period. AI-powered fraud-prediction networks utilize machine learning and artificial intelligence to analyze vast transactional datasets, detect patterns, and identify anomalies indicative of fraudulent activity in real time. These adaptive systems continuously learn new fraud strategies, minimize false positives, and automate alerts, bolstering protective measures for sectors such as banking, e-commerce, identity verification, and insurance-reducing economic losses and enhancing trust.

According to the Bank for International Settlements, consortium-based AI models that analyze transaction patterns across multiple banks are significantly more effective at detecting sophisticated, cross-institutional payment fraud.

Market Dynamics:

Driver:

Escalation of real-time transaction fraud

Escalation of real-time transaction fraud is intensifying enterprise demand for adaptive, AI-native prediction networks capable of detecting micro-anomalies at millisecond latency. Fueled by surging digital payments, cross-border e-commerce, and instant-settlement rails, financial institutions are prioritizing proactive fraud interdiction over reactive post-event investigations. Rising attack sophistication, especially across mobile wallets and embedded finance platforms, is accelerating platform modernization. Consequently, vendors are scaling graph-based inference engines to augment contextual decisioning and reduce false positives across continuously evolving threat landscapes.

Restraint:

High model drift in rapidly changing fraud signatures

High model drift in rapidly changing fraud signatures remains a critical barrier, as adversaries continuously alter behavioral patterns to evade detection. Spurred by volatile transaction streams and region-specific fraud vectors, supervised models often degrade without frequent re-training, imposing heavy operational overheads. This drift necessitates constant feature engineering, quality labeling, and pipeline recalibration, inflating cost structures for banks and fintechs. As a result, many organizations struggle to sustain reliable predictive performance, especially when fraud volumes spike unpredictably.

Opportunity:

Fusion of behavioral biometrics

Fusion of behavioral biometrics presents a compelling expansion pathway, enabling fraud-prediction networks to assess intent-driven micro-interactions beyond static credentials. Motivated by rising identity-theft cases and synthetic-ID fraud, institutions are integrating keystroke dynamics, gait patterns, touchscreen pressure, and navigation rhythms into multimodal fraud scoring engines. This convergence strengthens continuous authentication and enhances risk segmentation across high-velocity digital channels. Consequently, next-generation AI-risk platforms can deliver richer anomaly detection, reduce customer friction, and differentiate between legitimate users and orchestrated fraud attempts with higher precision.

Threat:

Adversarial AI undermining predictive accuracy

Adversarial AI undermining predictive accuracy poses a substantial threat, as malicious actors deploy generative models to craft attack patterns that mimic legitimate user behavior. Driven by the proliferation of automated fraud-as-a-service ecosystems, these adversarial agents manipulate model blind spots, degrade classifier reliability, and inflate false-negative rates. Additionally, targeted poisoning of training datasets can destabilize fraud-prevention pipelines. This escalating arms race forces vendors to embed robust model-hardening, constant adversarial testing, and resilient ensemble architectures to maintain defensive efficacy.

Covid-19 Impact:

Covid-19 accelerated the digitalization of payments, inadvertently triggering an unprecedented surge in phishing, account-takeover, and stimulus-fraud incidents. As remote onboarding and contactless transactions became mainstream, financial institutions adopted AI-fraud prediction tools to offset rising operational exposure. Heightened consumer vulnerability and reduced in-person verification fueled demand for automated risk-scoring engines and behavioral monitoring modules. Post-pandemic, fraud-prediction networks remain integral to safeguarding digital channels, with sustained investments in scalable cloud-native analytics and continuous identity assurance frameworks.

The fraud detection engines segment is expected to be the largest during the forecast period

The fraud detection engines segment is expected to account for the largest market share during the forecast period, resulting from their central role in orchestrating real-time anomaly scoring across high-velocity payment environments. Propelled by surging demand for deep-learning-based pattern recognition, these engines aggregate transactional, device, and behavioral telemetry to generate risk signals at scale. Their versatility across banking, insurance, and e-commerce ecosystems further solidifies dominance. Additionally, rapid enhancements in graph analytics and adaptive rule orchestration reinforce their market leadership.

The cloud-based systems segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based systems segment is predicted to witness the highest growth rate, propelled by enterprises shifting from legacy on-premise risk engines to elastic, API-driven fraud intelligence platforms. Accelerated by real-time transaction volumes and global payment flows, cloud architectures provide rapid model deployment, continuous updates, and cross-regional threat telemetry sharing. Their pay-as-you-scale economics and seamless integration with digital banking stacks further amplify adoption. This flexibility is especially valuable for fintechs and neo-banks requiring instant fraud-response capabilities.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to explosive growth in digital wallets, QR-based payments, and super-app ecosystems. Fueled by dense mobile penetration and rising cross-border remittance flows, the region faces elevated fraud exposure, prompting heavy investments in AI-centric risk-scoring frameworks. Additionally, regulatory bodies across India, Singapore, and Australia are mandating stronger authentication and fraud-monitoring controls. These dynamics position APAC as the most expansive deployment hub for real-time fraud-prediction networks.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, associated with rapid adoption of advanced fraud-intelligence platforms by banks, card networks, and digital-first lenders. Heightened cybercrime sophistication, coupled with aggressive regulatory scrutiny around consumer protection, is accelerating system upgrades. Furthermore, the region hosts leading AI-risk analytics vendors, enabling faster innovation cycles in adversarial detection, behavioral biometrics, and federated learning. Expanding fintech ecosystems and instant-payment rails further amplify demand for scalable, cloud-native fraud-prediction networks.

Key players in the market

Some of the key players in AI-Powered Fraud-Prediction Networks Market include FICO, Experian, NICE Actimize, SAS, LexisNexis Risk Solutions, Featurespace, Forter, Sift, Kount, Darktrace, DataVisor, Mastercard, Visa, PayPal, Feedzai, and ACI Worldwide.

Key Developments:

In September 2025, NICE Actimize introduced its Generative AI Suspicion Analyzer, a tool that uses advanced large language models to automatically analyze the context of suspicious activity reports (SARs) and customer interactions, dramatically reducing false positives and improving the accuracy of financial crime alerts.

In August 2025, Featurespace unveiled the ARIC(TM) Risk Hub for Real-Time Payments, a specialized AI model designed to analyze the unique risk patterns of instant payment rails like FedNow and RTP, preventing fraudulent transactions within the sub-second decision window.

In July 2025, Mastercard launched its "Consumer Fraud Risk" scoring service, an open-banking enabled AI network that allows merchants and issuers to share anonymized risk signals, providing a holistic view of a user's digital footprint to stop account takeover and friendly fraud.

Components Covered:

  • Fraud Detection Engines
  • Behavioral Analytics Modules
  • Identity Verification Systems
  • Transaction Monitoring Platforms
  • Risk-Scoring Models

Deployments Covered:

  • Cloud-Based Systems
  • On-Premise Platforms
  • Hybrid Infrastructure
  • Edge-AI Fraud Detection Nodes
  • Distributed Fraud Intelligence Networks

Applications Covered:

  • BFSI Fraud Management
  • E-Commerce Transaction Security
  • Identity & Access Fraud
  • Payment Gateway Monitoring
  • Digital Wallet Security

End Users Covered:

  • Municipal Water Utilities
  • Industrial Facilities
  • Marine
  • Environmental Agencies

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 Application 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-Prediction Networks Market, By Component

  • 5.1 Introduction
  • 5.2 Fraud Detection Engines
  • 5.3 Behavioral Analytics Modules
  • 5.4 Identity Verification Systems
  • 5.5 Transaction Monitoring Platforms
  • 5.6 Risk-Scoring Models

6 Global AI-Powered Fraud-Prediction Networks Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud-Based Systems
  • 6.3 On-Premise Platforms
  • 6.4 Hybrid Infrastructure
  • 6.5 Edge-AI Fraud Detection Nodes
  • 6.6 Distributed Fraud Intelligence Networks

7 Global AI-Powered Fraud-Prediction Networks Market, By Application

  • 7.1 Introduction
  • 7.2 BFSI Fraud Management
  • 7.3 E-Commerce Transaction Security
  • 7.4 Identity & Access Fraud
  • 7.5 Payment Gateway Monitoring
  • 7.6 Digital Wallet Security

8 Global AI-Powered Fraud-Prediction Networks Market, By End User

  • 8.1 Introduction
  • 8.2 Banks & NBFCs
  • 8.3 E-Commerce Companies
  • 8.4 Fintech Firms
  • 8.5 Telecom Operators
  • 8.6 Insurance Providers

9 Global AI-Powered Fraud-Prediction Networks Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 FICO
  • 11.2 Experian
  • 11.3 NICE Actimize
  • 11.4 SAS
  • 11.5 LexisNexis Risk Solutions
  • 11.6 Featurespace
  • 11.7 Forter
  • 11.8 Sift
  • 11.9 Kount
  • 11.10 Darktrace
  • 11.11 DataVisor
  • 11.12 Mastercard
  • 11.13 Visa
  • 11.14 PayPal
  • 11.15 Feedzai
  • 11.16 ACI Worldwide

List of Tables

  • Table 1 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Fraud Detection Engines (2024-2032) ($MN)
  • Table 4 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Behavioral Analytics Modules (2024-2032) ($MN)
  • Table 5 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Identity Verification Systems (2024-2032) ($MN)
  • Table 6 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Transaction Monitoring Platforms (2024-2032) ($MN)
  • Table 7 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Risk-Scoring Models (2024-2032) ($MN)
  • Table 8 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 9 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Cloud-Based Systems (2024-2032) ($MN)
  • Table 10 Global AI-Powered Fraud-Prediction Networks Market Outlook, By On-Premise Platforms (2024-2032) ($MN)
  • Table 11 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Hybrid Infrastructure (2024-2032) ($MN)
  • Table 12 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Edge-AI Fraud Detection Nodes (2024-2032) ($MN)
  • Table 13 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Distributed Fraud Intelligence Networks (2024-2032) ($MN)
  • Table 14 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Application (2024-2032) ($MN)
  • Table 15 Global AI-Powered Fraud-Prediction Networks Market Outlook, By BFSI Fraud Management (2024-2032) ($MN)
  • Table 16 Global AI-Powered Fraud-Prediction Networks Market Outlook, By E-Commerce Transaction Security (2024-2032) ($MN)
  • Table 17 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Identity & Access Fraud (2024-2032) ($MN)
  • Table 18 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Payment Gateway Monitoring (2024-2032) ($MN)
  • Table 19 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Digital Wallet Security (2024-2032) ($MN)
  • Table 20 Global AI-Powered Fraud-Prediction Networks Market Outlook, By End User (2024-2032) ($MN)
  • Table 21 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Banks & NBFCs (2024-2032) ($MN)
  • Table 22 Global AI-Powered Fraud-Prediction Networks Market Outlook, By E-Commerce Companies (2024-2032) ($MN)
  • Table 23 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Fintech Firms (2024-2032) ($MN)
  • Table 24 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Telecom Operators (2024-2032) ($MN)
  • Table 25 Global AI-Powered Fraud-Prediction Networks Market Outlook, By Insurance Providers (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.