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1857050

全球基於人工智慧的通訊詐騙偵測市場：預測至 2032 年—按組件、部署方式、詐騙類型、組織規模、技術和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧通訊詐騙偵測市場預計到 2025 年將達到 36 億美元，到 2032 年將達到 137 億美元，預測期內複合年成長率為 21.0%。

人工智慧驅動的通訊詐騙偵測系統利用人工智慧和機器學習技術，即時辨識並預防通訊詐騙。這些系統能夠偵測出諸如SIM卡盒裝、訂閱詐騙和國際收入分成詐騙（IRSF）等詐騙模式。透過分析大量的通話資料記錄，這些系統可以標記異常情況並主動攔截威脅。隨著通訊詐騙日益複雜，通訊業者正在採用這些解決方案來保護收入、保障客戶帳戶安全並最大限度地減少經濟損失。

根據美國聯邦通訊委員會(FCC) 的數據，人工智慧驅動的通訊詐騙偵測系統在 2022 年至 2024 年間將 SIM 卡交換詐騙案件減少了 28%。

日益複雜的通訊詐騙手段

通訊詐騙手段日益複雜，包括SIM卡交換攻擊、網路釣魚和訂閱詐騙等，這推動了對人工智慧偵測解決方案的需求。傳統的基於規則的系統難以偵測不斷演變的詐欺模式，促使通訊業者採用先進的人工智慧和機器學習模型，即時分析大量的通話、交易和網路數據。此外，監管機構為保護客戶資料和防止經濟損失而施加的壓力也推動了相關投資，使得先進的人工智慧檢測成為通訊業營運安全和服務可靠性的關鍵組成部分。

人工智慧系統的高昂實施成本

部署基於人工智慧的詐騙偵測系統需要在基礎設施、資料管理和專業人才方面進行大量投資。通訊業者，尤其是在新興市場，面臨預算限制，這限制了部署的規模和速度。高昂的前期成本、持續的維護以及與舊有系統的整合，都可能阻礙小規模業者採用先進的解決方案。此外，持續的模型訓練和更新需求也帶來了持續的支出和財務挑戰。儘管全球對強大的詐騙預防解決方案的需求日益成長，但這些成本障礙正在減緩市場滲透率。

擴展到物聯網安全和行動銀行保護

物聯網設備和行動銀行服務的普及為人工智慧驅動的通訊詐騙偵測服務供應商帶來了巨大的機會。隨著連網裝置和行動交易的增加，詐騙風險也隨之擴大，從而催生了對先進的即時監控和預測分析的需求。企業可以開發專門的解決方案來保護物聯網網路、智慧設備和行動金融服務，從而開闢新的收入來源。此外，與銀行、金融科技公司和物聯網服務供應商建立合作關係，能夠幫助供應商實現產品多元化，提高用戶採納率，並在快速發展的數位生態系統中獲得長期的策略立足點。

不斷演變的詐騙手段旨在規避現有的檢測模型

詐騙不斷開發新的策略來規避現有的人工智慧偵測系統，包括社交工程、深度造假電話和匿名網路攻擊。這種快速演變對已部署模型的有效性構成挑戰，需要持續進行重新訓練、演算法改進以及整合更多威脅情報。此外，偵測機制更新的延遲可能會對通訊業者造成重大的經濟和聲譽損失。

新冠疫情的影響：

疫情加速了數位化，並加劇了人們對通訊和行動服務的依賴，同時也無意中增加了詐騙風險。遠距辦公、線上交易和行動銀行的普及為詐騙創造了新的攻擊途徑。因此，通訊業者加快了人工智慧驅動的詐騙偵測解決方案的部署，以保護客戶並保障自身收入。此外，網路威脅的激增凸顯了即時監控和預測分析的重要性，也強化了對能夠應對瞬息萬變的通訊詐騙模式的先進人工智慧模型的需求。

預計在預測期內，雲端基礎市場將成為最大的細分市場。

由於其成本效益高、易於部署且擴充性以適應不斷擴展的通訊網路，預計在預測期內，雲端基礎方案將佔據最大的市場佔有率。與本地部署系統相比，通訊業者可受益於持續更新、增強的分析能力和更低的維護成本。此外，雲端基礎設施支援處理大量數據，這對於偵測進階詐騙模式至關重要。營運效率、安全性和適應性的完美結合，使得雲端基礎的解決方案能夠佔據最大的市場佔有率，同時滿足全球不斷發展的通訊業的需求。

預計服務業在預測期內將實現最高的複合年成長率。

由於對端到端解決方案（包括人工智慧模型開發、系統整合和持續支援）的需求不斷成長，預計服務板塊在預測期內將實現最高成長率。營運商正在尋求專業知識，以實施強大的詐騙檢測框架，確保合規性，並適應快速演變的詐騙手段。此外，服務提供者還提供分析、監控和最佳化工具，這些工具無需投入大量內部技術資源即可提升效能。這一趨勢反映了成熟通訊地區和通訊電信地區的市場擴張潛力，使服務部門成為複合年成長率最高的板塊。

佔比最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其先進的通訊基礎設施、人工智慧技術的高度普及以及在詐騙方面的大量投資。該地區的通訊業者面臨嚴格的法律規範和複雜的詐騙手段，這推動了人工智慧解決方案的部署。此外，主要技術供應商的存在以及人工智慧和分析領域的持續創新也促進了市場的成熟和競爭。這些因素共同作用，將使北美在企業通訊業者和行動服務供應商的強勁需求驅動下，保持最大的市場佔有率。

複合年成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率。通訊網路的快速擴張、智慧型手機普及率的不斷提高以及行動銀行的日益普及，正在推動亞太地區對人工智慧驅動的詐騙檢測技術的需求。新興經濟體面臨著不斷演變的詐騙手段帶來的日益嚴峻的風險，促使營運商投資可擴展的雲端基礎人工智慧解決方案。此外，政府支持數位轉型的舉措，以及物聯網應用的日益普及，共同為市場成長創造了有利環境。這些因素共同推動亞太地區實現最高的複合年成長率，反映了全部區域強勁的市場接受度和巨大的市場潛力。

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    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球人工智慧通訊詐騙偵測市場（按組件分類）

  • 解決方案/平台
  • 服務
    • 專業服務
    • 託管服務

6. 全球人工智慧賦能通訊詐騙偵測市場（依部署方式分類）

  • 本地部署
  • 雲端基礎的

7. 全球人工智慧賦能通訊詐騙偵測市場（依詐騙類型分類）

  • 訂閱詐騙
  • 收入分成詐騙（IRSF）
  • 一響即掛的電話詐騙
  • PBX駭客攻擊
  • SIM卡盒詐騙（繞過詐騙）
  • 漫遊詐騙
  • 新帳戶詐騙
  • 其他類型的詐騙

8. 全球人工智慧賦能通訊詐騙偵測市場（依組織規模分類）

  • 主要企業
  • 小型企業

9. 全球人工智慧通訊詐騙偵測市場（按技術分類）

  • 機器學習（ML）和深度學習（DL）
  • 自然語言處理（NLP）
  • 巨量資料分析
  • 行為分析
  • 其他技術

第10章 全球人工智慧通訊詐騙偵測市場（按地區分類）

  • 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
    • 其他南美洲
  • 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11章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章：公司簡介

  • 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.