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市场调查报告书
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1880430

人工智慧驱动的诈欺预测网路市场预测至2032年：按组件、部署类型、应用、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的一项研究，全球人工智慧驱动的诈欺预测网路市场预计到 2025 年将达到 108 亿美元，到 2032 年将达到 386 亿美元，在预测期内以 20% 的复合年增长率成长。

人工智慧驱动的诈欺预测网路利用机器学习和人工智慧技术分析大量交易资料集，即时侦测模式并识别异常情况，从而发现诈欺活动。这些自适应系统不断学习新的诈欺手段，最大限度地减少误报，并自动发出警报，从而提高安全性，减少经济损失，并增强银行、电子商务、身份验证和保险等行业的信任度。

根据国际清算银行（BIS）的说法，分析多家银行交易模式的联盟人工智慧模型在检测复杂的跨机构支付诈骗要有效得多。

即时交易诈骗呈上升趋势。

即时交易诈骗的日益猖獗，推动了企业对能够以毫秒级延迟检测细微异常的自适应、原生人工智慧预测网路的需求。数位支付、跨境电商和即时支付系统的普及，促使金融机构将主动防范诈欺置于被动调查之上。攻击手段的日益复杂化，尤其是针对行动钱包和嵌入式金融平台的攻击，正在加速平台现代化进程。因此，供应商正在扩展其基于图的推理引擎，以在不断演变的威胁环境中提升情境决策能力并减少误报。

由于诈欺特征快速变化，导致模型漂移较大

由于攻击者不断改变其行为模式以逃避检测，快速变化的诈欺特征导致模型漂移严重，这仍然是一个重大障碍。由于交易流程高度多变且欺诈手段具有地域性，监督式模型若不频繁重新训练，性能往往会下降，从而给营运带来沉重负担。这种漂移需要持续的特征工程、高品质的标註和管道调整，推高了银行和金融科技公司的成本结构。因此，许多机构难以维持可靠的预测效能，尤其是在诈欺量出现不可预测的激增时。

行为生物辨识技术的融合

行为生物特征技术的融合为扩展诈骗预测网络提供了一条强有力的途径，使其超越静态凭证，能够评估意图驱动的、细緻入微的互动行为。在身分盗窃和合成身分诈骗猖獗的推动下，金融机构正将击键动态、步态模式、触控萤幕压力和互动节奏等资讯整合到多模态诈骗评分引擎中。这种融合增强了持续身份验证，并改善了高速数位管道中的风险细分。因此，下一代人工智慧风险平台能够在不影响客户便利性的前提下，更准确地侦测异常情况，区分合法使用者和有组织的诈骗行为。

对抗性人工智慧会降低预测准确性

对抗性人工智慧正在削弱预测准确率，构成重大威胁，因为恶意攻击者会利用生成模型来建构模仿合法使用者行为的攻击模式。在自动化「诈骗即服务」生态系统的推动下，这些对抗性代理会利用模型盲点，降低分类器的可靠性并增加漏报率。此外，针对训练资料集的定向投毒会破坏诈骗防制流程。这场不断升级的军备竞赛迫使供应商采用强大的模型加固、持续的对抗性测试和容错集成架构来维持防御效力。

新冠疫情的影响：

感染疾病加速了支付数位化，却也无意间导致了网路钓鱼、帐号盗用和纾困金诈骗等犯罪活动的空前激增。随着远端开户和非接触式交易的普及，金融机构部署了人工智慧驱动的诈欺预测工具，以应对日益增长的营运风险。消费者脆弱性的增加和麵对面身份验证的减少，加速了对自动化风险评分引擎和行为监控模组的需求。即使在疫情结束后，诈欺预测网路对于保障数位管道的安全仍然至关重要，因此，可扩展的云端原生分析和持续身分验证框架的投资仍在持续。

预计在预测期内，诈欺侦测引擎细分市场将占据最大的市场份额。

由于诈欺侦测引擎在高速支付环境中即时异常评分方面发挥核心作用，预计在预测期内，诈欺侦测引擎细分市场将占据最大的市场份额。在对基于深度学习的模式识别日益增长的需求驱动下，这些引擎聚合交易数据、设备资讯和行为遥测数据，从而大规模产生风险讯号。它们在银行、保险和电子商务生态系统中的广泛应用进一步巩固了其市场主导地位。此外，图分析和自适应规则编配的快速发展也进一步强化了主导地位。

预计在预测期内，云端基础的系统细分市场将呈现最高的复合年增长率。

在预测期内，云端基础的系统领域预计将实现最高成长率，这主要得益于各机构从传统的本地风险引擎迁移到扩充性的、API驱动的诈欺侦测智慧平台。凭藉即时交易量和加速的全球支付流程，云端架构能够实现快速模型部署、持续更新以及跨区域威胁遥测资料共用。计量收费的经济模式以及与数位银行系统的无缝整合将进一步推动云端架构的普及。这种灵活性对于需要即时诈欺应变能力的金融科技公司和新型银行而言尤其重要。

占比最大的地区：

由于数位钱包、QR码支付和超级应用生态系统的爆炸性增长，亚太地区预计将在预测期内占据最大的市场份额。在高行动普及率和不断增长的跨境汇款流量的推动下，该地区的诈骗风险日益上升，促使各方对人工智慧驱动的风险评分框架进行大量投资。此外，印度、新加坡和澳洲的监管机构正在强制要求更严格的身份验证和诈骗监控控制措施。这些趋势使亚太地区成为即时诈骗预测网路应用最广泛的地区。

年复合成长率最高的地区：

预计在预测期内，北美将实现最高的复合年增长率，这主要得益于银行、卡组织和数位化优先贷款机构对先进诈欺检测平台的快速采用。日益复杂的网路犯罪以及消费者保护日益严格的监管审查，正在加速系统升级。此外，该地区汇聚了许多领先的人工智慧风险分析供应商，从而加快了对抗性检测、行为生物识别和联邦学习等领域的创新週期。不断发展的金融科技生态系统和即时支付基础设施进一步提升了对可扩展的云端原生诈欺预测网路的需求。

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  • 公司概况
    • 对其他市场公司（最多 3 家公司）进行全面分析
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    • 根据主要企业的产品系列、地理覆盖范围和策略联盟基准化分析

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
    • 原始研究资料
    • 次级研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

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

第五章 全球人工智慧驱动的诈欺预测网路市场（按组件划分）

  • 介绍
  • 诈骗侦测引擎
  • 行为分析模组
  • 身份验证系统
  • 交易监控平台
  • 风险评分模型

第六章 全球基于人工智慧的诈欺预测网路市场（按实施类型划分）

  • 介绍
  • 云端基础的系统
  • 本地部署平台
  • 混合基础设施
  • 边缘人工智慧诈骗侦测节点
  • 分散式诈骗网络

7. 全球基于人工智慧的诈欺预测网路市场（按应用划分）

  • 介绍
  • 金融服务业欺诈管理
  • 电子商务交易安全
  • 身份和存取诈骗
  • 付款闸道监控
  • 数位钱包安全

第八章 全球人工智慧驱动的诈欺预测网路市场（按最终用户划分）

  • 介绍
  • 银行和非银行金融公司
  • 电子商务企业
  • 金融科技公司
  • 通讯业者
  • 保险公司

9. 全球人工智慧驱动的诈欺预测网路市场（按地区划分）

  • 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
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 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

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • 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.