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市场调查报告书
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1857050

全球基于人工智慧的通讯诈骗侦测市场：预测至 2032 年—按组件、部署方式、诈骗类型、组织规模、技术和地区进行分析

AI-Powered Telecom Fraud Detection Market Forecasts to 2032 - Global Analysis By Component (Solutions/Platforms, and Services), Deployment Mode (On-Premises, and Cloud-Based), Fraud Type, Organization Size, Technology, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球人工智慧通讯诈骗侦测市场预计到 2025 年将达到 36 亿美元，到 2032 年将达到 137 亿美元，预测期内复合年增长率为 21.0%。

人工智慧驱动的通讯诈骗侦测系统利用人工智慧和机器学习技术，即时辨识并预防通讯诈骗。这些系统能够侦测出诸如SIM卡盒装、订阅诈骗和国际收入分成诈骗（IRSF）等诈骗模式。透过分析大量的通话资料记录，这些系统可以标记异常情况并主动拦截威胁。随着通讯诈骗日益复杂，通讯业者正在采用这些解决方案来保护收入、保障客户帐户安全并最大限度地减少经济损失。

根据美国联邦通讯委员会(FCC) 的数据，人工智慧驱动的通讯诈骗侦测系统在 2022 年至 2024 年间将 SIM 卡交换诈骗案件减少了 28%。

日益复杂的通讯诈骗手段

通讯诈骗手段日益复杂，包括SIM卡交换攻击、网路钓鱼和订阅诈骗等，这推动了对人工智慧侦测解决方案的需求。传统的基于规则的系统难以侦测不断演变的诈欺模式，促使通讯业者采用先进的人工智慧和机器学习模型，即时分析大量的通话、交易和网路数据。此外，监管机构为保护客户资料和防止经济损失而施加的压力也推动了相关投资，使得先进的人工智慧检测成为通讯业营运安全和服务可靠性的关键组成部分。

人工智慧系统的高昂实施成本

部署基于人工智慧的诈骗侦测系统需要在基础设施、资料管理和专业人才方面进行大量投资。通讯业者，尤其是在新兴市场，面临预算限制，这限制了部署的规模和速度。高昂的前期成本、持续的维护以及与旧有系统的集成，都可能阻碍小规模业者采用先进的解决方案。此外，持续的模型训练和更新需求也带来了持续的支出和财务挑战。儘管全球对强大的诈骗预防解决方案的需求日益增长，但这些成本障碍正在减缓市场渗透率。

扩展到物联网安全和行动银行保护

物联网设备和行动银行服务的普及为人工智慧驱动的通讯诈骗侦测服务供应商带来了巨大的机会。随着连网装置和行动交易的增加，诈骗风险也随之扩大，从而催生了对先进的即时监控和预测分析的需求。企业可以开发专门的解决方案来保护物联网网路、智慧设备和行动金融服务，从而开闢新的收入来源。此外，与银行、金融科技公司和物联网服务供应商建立合作关係，能够帮助供应商实现产品多元化，提高用户采纳率，并在快速发展的数位生态系统中获得长期的策略立足点。

不断演变的诈骗手段旨在规避现有的检测模型

诈骗不断开发新的策略来规避现有的人工智慧侦测系统，包括社交工程、深度造假电话和匿名网路攻击。这种快速演变对已部署模型的有效性构成挑战，需要持续进行重新训练、演算法改进以及整合更多威胁情报。此外，侦测机制更新的延迟可能会对通讯业者造成重大的经济和声誉损失。

新冠疫情的影响：

疫情加速了数位化，并加剧了人们对通讯和行动服务的依赖，同时也无意中增加了诈骗风险。远距办公、线上交易和行动银行的普及为诈骗创造了新的攻击途径。因此，通讯业者加快了人工智慧驱动的诈骗侦测解决方案的部署，以保护客户并保障自身收入。此外，网路威胁的激增凸显了即时监控和预测分析的重要性，也强化了对能够应对瞬息万变的通讯诈骗模式的先进人工智慧模型的需求。

预计在预测期内，云端基础市场将成为最大的细分市场。

由于其成本效益高、易于部署且扩充性以适应不断扩展的通讯网络，预计在预测期内，云端基础方案将占据最大的市场份额。与本地部署系统相比，通讯业者可受益于持续更新、增强的分析能力和更低的维护成本。此外，云端基础设施支援处理大量数据，这对于侦测进阶诈骗模式至关重要。营运效率、安全性和适应性的完美结合，使得云端基础的解决方案能够占据最大的市场份额，同时满足全球不断发展的通讯业的需求。

预计服务业在预测期内将实现最高的复合年增长率。

由于对端到端解决方案（包括人工智慧模型开发、系统整合和持续支援）的需求不断增长，预计服务板块在预测期内将实现最高成长率。营运商正在寻求专业知识，以实施强大的诈骗检测框架，确保合规性，并适应快速演变的诈骗手段。此外，服务提供者还提供分析、监控和最佳化工具，这些工具无需投入大量内部技术资源即可提升效能。这一趋势反映了成熟通讯地区和通讯电信地区的市场扩张潜力，使服务部门成为复合年增长率最高的板块。

占比最大的地区：

在预测期内，北美预计将占据最大的市场份额，这主要得益于其先进的通讯基础设施、人工智慧技术的高度普及以及在诈骗方面的大量投资。该地区的通讯业者面临严格的法律规范和复杂的诈骗手段，这推动了人工智慧解决方案的部署。此外，主要技术供应商的存在以及人工智慧和分析领域的持续创新也促进了市场的成熟和竞争。这些因素共同作用，将使北美在企业通讯业者和行动服务供应商的强劲需求驱动下，保持最大的市场份额。

复合年增长率最高的地区：

预计亚太地区在预测期内将实现最高的复合年增长率。通讯网路的快速扩张、智慧型手机普及率的不断提高以及行动银行的日益普及，正在推动亚太地区对人工智慧驱动的诈骗检测技术的需求。新兴经济体面临着不断演变的诈骗手段带来的日益严峻的风险，促使营运商投资可扩展的云端基础人工智慧解决方案。此外，政府支持数位转型的倡议，以及物联网应用的日益普及，共同为市场成长创造了有利环境。这些因素共同推动亚太地区实现最高的复合年增长率，反映了全部区域强劲的市场接受度和巨大的市场潜力。

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  • 公司简介
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    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
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    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球人工智慧通讯诈骗侦测市场（按组件划分）

  • 解决方案/平台
  • 服务
    • 专业服务
    • 託管服务

6. 全球人工智慧赋能通讯诈骗侦测市场（依部署方式划分）

  • 本地部署
  • 云端基础的

7. 全球人工智慧赋能通讯诈骗侦测市场（依诈骗类型划分）

  • 订阅诈骗
  • 收入分成诈骗（IRSF）
  • 一响即挂的电话诈骗
  • PBX骇客攻击
  • SIM卡盒诈骗（绕过诈骗）
  • 漫游诈骗
  • 新帐户诈骗
  • 其他类型的诈骗

8. 全球人工智慧赋能通讯诈骗侦测市场（依组织规模划分）

  • 大公司
  • 小型企业

9. 全球人工智慧通讯诈骗侦测市场（按技术划分）

  • 机器学习（ML）和深度学习（DL）
  • 自然语言处理（NLP）
  • 巨量资料分析
  • 行为分析
  • 其他技术

第十章 全球人工智慧通讯诈骗侦测市场（按地区划分）

  • 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
    • 其他南美洲
  • 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

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • Subex Limited
  • Socure Inc.
  • Neural Technologies Limited
  • Vonage Holdings Corp.
  • HCLTech
  • SAS Institute Inc.
  • Inform Software
  • Sift Science, Inc.
  • Quantexa Limited
  • Feedzai Inc.
  • Seon Technologies
  • Tanla Platforms Limited
  • Airtel Limited
  • Vodafone Idea Limited
  • Mastercard Incorporated
Product Code: SMRC31915

According to Stratistics MRC, the Global AI-Powered Telecom Fraud Detection Market is accounted for $3.6 billion in 2025 and is expected to reach $13.7 billion by 2032 growing at a CAGR of 21.0% during the forecast period. AI-powered telecom fraud detection provides systems that use AI and ML to identify and prevent fraudulent activities in telecommunications in real-time. It detects patterns indicative of fraud like SIM boxing, subscription fraud, or international revenue share fraud (IRSF). By analyzing vast call data records, these systems can flag anomalies and block threats proactively. As telecom fraud becomes more sophisticated, carriers adopt these solutions to protect revenue and secure customer accounts, minimizing financial losses.

According to the Federal Communications Commission (FCC), AI-powered telecom fraud detection systems decreased SIM-swapping fraud incidents by 28% between 2022 and 2024.

Market Dynamics:

Driver:

Rising sophistication of telecom fraud schemes

The increasing complexity of telecom fraud, including SIM swap attacks, phishing, and subscription fraud, has intensified the need for AI-powered detection solutions. Traditional rule-based systems struggle to detect evolving patterns, prompting telecom operators to adopt advanced AI and machine learning models that analyze large volumes of call, transaction, and network data in real time. Furthermore, regulatory pressure to safeguard customer data and prevent financial losses drives investments, making sophisticated AI detection a critical component for operational security and service reliability across the telecom industry.

Restraint:

High implementation costs for AI systems

Deploying AI-based fraud detection requires substantial investment in infrastructure, data management, and skilled personnel. Telecom operators, especially in emerging markets, face budgetary constraints that limit the scale and speed of adoption. High upfront costs, ongoing maintenance, and integration with legacy systems can deter smaller providers from implementing advanced solutions. Additionally, the need for continuous model training and updates adds recurring expenses, posing financial challenges. These cost barriers can slow market penetration despite the growing necessity for robust fraud prevention solutions globally.

Opportunity:

Expansion into IoT security and mobile banking protection

The proliferation of IoT devices and mobile banking services presents significant opportunities for AI-powered telecom fraud detection providers. As connected devices and mobile transactions increase, the risk of fraud expands, creating demand for advanced real-time monitoring and predictive analytics. Companies can develop specialized solutions to secure IoT networks, smart devices, and mobile financial services, offering additional revenue streams. Moreover, partnerships with banks, fintechs, and IoT service providers allow vendors to diversify their offerings, enhance adoption, and establish long-term strategic footholds in rapidly growing digital ecosystems.

Threat:

Evolving fraud tactics bypassing existing detection models

Fraudsters continuously develop new strategies to circumvent existing AI detection systems, including sophisticated social engineering, deepfake calls, and anonymized network attacks. This rapid evolution challenges the effectiveness of deployed models, requiring continuous retraining, algorithm refinement, and integration of additional threat intelligence. Moreover, delays in updating detection mechanisms can lead to significant financial losses and reputational damage for telecom operators.

Covid-19 Impact:

The pandemic accelerated digital adoption, increasing reliance on telecom and mobile services, which inadvertently raised exposure to fraud. Remote work, online transactions, and heightened mobile banking usage created new attack vectors for fraudsters. Consequently, telecom operators accelerated deployment of AI-powered fraud detection solutions to protect customers and safeguard revenue. Additionally, the surge in cyber threats highlighted the critical importance of real-time monitoring and predictive analytics, reinforcing demand for advanced AI models capable of responding to evolving telecom fraud patterns under rapidly changing circumstances.

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

The cloud-based segment is expected to account for the largest market share during the forecast period is driven by their cost-effectiveness, ease of deployment, and ability to scale with growing telecom networks. Operators benefit from continuous updates, enhanced analytics, and reduced maintenance overhead compared to on-premise systems. Furthermore, cloud infrastructure supports high-volume data processing essential for detecting sophisticated fraud patterns. The combination of operational efficiency, security, and adaptability ensures that cloud-based solutions capture the largest market share while meeting evolving telecom industry demands globally.

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 is fueled by increasing demand for end-to-end solutions encompassing AI model development, system integration, and ongoing support. Operators seek expertise to implement robust fraud detection frameworks, ensure regulatory compliance, and adapt to rapidly evolving fraud tactics. Moreover, service providers offer analytics, monitoring, and optimization tools that enhance performance without requiring heavy in-house technical resources. This trend positions services as the segment with the highest CAGR, reflecting strong market expansion potential in both mature and emerging telecom regions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to advanced telecom infrastructure, high adoption of AI technologies, and significant investment in fraud prevention. Regional operators face stringent regulatory frameworks and sophisticated fraud schemes, driving the deployment of AI-powered solutions. Additionally, the presence of major technology vendors and continuous innovation in AI and analytics contribute to a mature and competitive market. These factors collectively ensure North America maintains the largest market share, with strong demand from both enterprise telecom operators and mobile service providers.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid telecom network expansion, growing smartphone penetration, and increasing mobile banking adoption fuel the demand for AI-powered fraud detection in Asia Pacific. Emerging economies face heightened risks from evolving fraud tactics, prompting operators to invest in scalable, cloud-based AI solutions. Additionally, government initiatives supporting digital transformation, coupled with increasing IoT deployments, create a fertile environment for market growth. These factors collectively drive Asia Pacific to achieve the highest CAGR, reflecting robust adoption and market potential across the region.

Key players in the market

Some of the key players in AI-Powered Telecom Fraud Detection Market include Subex Limited, Socure Inc., Neural Technologies Limited, Vonage Holdings Corp., HCLTech, SAS Institute Inc., Inform Software, Sift Science, Inc., Quantexa Limited, Feedzai Inc., Seon Technologies, Tanla Platforms Limited, Airtel Limited, Vodafone Idea Limited, and Mastercard Incorporated.

Key Developments:

In October 2025, HCLTech and Zscaler expanded their partnership to provide AI-powered security and network solutions. The integration of Zscaler's Zero Trust Exchange(TM) platform with HCLTech's Cybersecurity Fusion Center aims to enhance enterprise resilience and achieve business outcomes with a cloud-first, scalable security solution.

In September 2025, INFORM showcased its RiskShield software at Sibos 2025, combining machine learning with knowledge-based approaches to detect suspicious patterns in real-time and stop fraud. The platform offers an interconnected approach to fraud prevention, reflecting the collaborative spirit of Sibos.

In June 2025, Subex launched FraudZap(TM), a lightweight, AI-powered fraud detection platform designed to help telecom operator's combat fast-evolving fraud with unmatched speed and agility. The platform's first out-of-the-box use case targets the growing threat of handset fraud, one of the most pervasive challenges for telcos currently.

In June 2025, Subex integrated Embedded Generative AI into its HyperSense Revenue Assurance & Fraud Management platform, marking a foundational shift in how telecom systems operate: moving from static configuration to dynamic, AI-driven reasoning.

Components Covered:

  • Solutions/Platforms
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based

Fraud Types Covered:

  • Subscription Fraud
  • Revenue Share Fraud (IRSF)
  • Wangiri Fraud (One-Ring Scam)
  • PBX Hacking
  • SIM Box Fraud (Bypass Fraud)
  • Roaming Fraud
  • New Account Fraud
  • Other Fraud Types

Organization Sizes Covered:

  • Large Enterprises
  • Small and Medium-sized Enterprises

Technologies Covered:

  • Machine Learning (ML) & Deep Learning (DL)
  • Natural Language Processing (NLP)
  • Big Data Analytics
  • Behavioral Analytics
  • Other AI Subsets

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 Emerging Markets
  • 3.8 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 Telecom Fraud Detection Market, By Component

  • 5.1 Introduction
  • 5.2 Solutions/Platforms
  • 5.3 Services
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services

6 Global AI-Powered Telecom Fraud Detection Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Based

7 Global AI-Powered Telecom Fraud Detection Market, By Fraud Type

  • 7.1 Introduction
  • 7.2 Subscription Fraud
  • 7.3 Revenue Share Fraud (IRSF)
  • 7.4 Wangiri Fraud (One-Ring Scam)
  • 7.5 PBX Hacking
  • 7.6 SIM Box Fraud (Bypass Fraud)
  • 7.7 Roaming Fraud
  • 7.8 New Account Fraud
  • 7.9 Other Fraud Types

8 Global AI-Powered Telecom Fraud Detection Market, By Organization Size

  • 8.1 Introduction
  • 8.2 Large Enterprises
  • 8.3 Small and Medium-sized Enterprises

9 Global AI-Powered Telecom Fraud Detection Market, By Technology

  • 9.1 Introduction
  • 9.2 Machine Learning (ML) & Deep Learning (DL)
  • 9.3 Natural Language Processing (NLP)
  • 9.4 Big Data Analytics
  • 9.5 Behavioral Analytics
  • 9.6 Other AI Subsets

10 Global AI-Powered Telecom Fraud Detection Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Subex Limited
  • 12.2 Socure Inc.
  • 12.3 Neural Technologies Limited
  • 12.4 Vonage Holdings Corp.
  • 12.5 HCLTech
  • 12.6 SAS Institute Inc.
  • 12.7 Inform Software
  • 12.8 Sift Science, Inc.
  • 12.9 Quantexa Limited
  • 12.10 Feedzai Inc.
  • 12.11 Seon Technologies
  • 12.12 Tanla Platforms Limited
  • 12.13 Airtel Limited
  • 12.14 Vodafone Idea Limited
  • 12.15 Mastercard Incorporated

List of Tables

  • Table 1 Global AI-Powered Telecom Fraud Detection Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Telecom Fraud Detection Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Telecom Fraud Detection Market Outlook, By Solutions/Platforms (2024-2032) ($MN)
  • Table 4 Global AI-Powered Telecom Fraud Detection Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI-Powered Telecom Fraud Detection Market Outlook, By Professional Services (2024-2032) ($MN)
  • Table 6 Global AI-Powered Telecom Fraud Detection Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 7 Global AI-Powered Telecom Fraud Detection Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 8 Global AI-Powered Telecom Fraud Detection Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 9 Global AI-Powered Telecom Fraud Detection Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 10 Global AI-Powered Telecom Fraud Detection Market Outlook, By Fraud Type (2024-2032) ($MN)
  • Table 11 Global AI-Powered Telecom Fraud Detection Market Outlook, By Subscription Fraud (2024-2032) ($MN)
  • Table 12 Global AI-Powered Telecom Fraud Detection Market Outlook, By Revenue Share Fraud (IRSF) (2024-2032) ($MN)
  • Table 13 Global AI-Powered Telecom Fraud Detection Market Outlook, By Wangiri Fraud (One-Ring Scam) (2024-2032) ($MN)
  • Table 14 Global AI-Powered Telecom Fraud Detection Market Outlook, By PBX Hacking (2024-2032) ($MN)
  • Table 15 Global AI-Powered Telecom Fraud Detection Market Outlook, By SIM Box Fraud (Bypass Fraud) (2024-2032) ($MN)
  • Table 16 Global AI-Powered Telecom Fraud Detection Market Outlook, By Roaming Fraud (2024-2032) ($MN)
  • Table 17 Global AI-Powered Telecom Fraud Detection Market Outlook, By New Account Fraud (2024-2032) ($MN)
  • Table 18 Global AI-Powered Telecom Fraud Detection Market Outlook, By Other Fraud Types (2024-2032) ($MN)
  • Table 19 Global AI-Powered Telecom Fraud Detection Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 20 Global AI-Powered Telecom Fraud Detection Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 21 Global AI-Powered Telecom Fraud Detection Market Outlook, By Small and Medium-sized Enterprises (2024-2032) ($MN)
  • Table 22 Global AI-Powered Telecom Fraud Detection Market Outlook, By Technology (2024-2032) ($MN)
  • Table 23 Global AI-Powered Telecom Fraud Detection Market Outlook, By Machine Learning (ML) & Deep Learning (DL) (2024-2032) ($MN)
  • Table 24 Global AI-Powered Telecom Fraud Detection Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 25 Global AI-Powered Telecom Fraud Detection Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 26 Global AI-Powered Telecom Fraud Detection Market Outlook, By Behavioral Analytics (2024-2032) ($MN)
  • Table 27 Global AI-Powered Telecom Fraud Detection Market Outlook, By Other AI Subsets (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.