查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2021727

以金融服務為導向的AI驅動型詐騙偵測市場：預測（至2034年）－按組件、詐欺類型、技術、部署方式、應用、最終用戶和地區進行分析

AI-Powered Fraud Detection in Financial Services Market Forecasts to 2034 - Global Analysis By Component (Solutions and Services), Fraud Type, Technology, Deployment Mode, 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，預計到 2026 年，全球金融服務領域人工智慧驅動的詐騙偵測市場規模將達到 63 億美元，並在預測期內以 21.9% 的複合年成長率成長，到 2034 年將達到 308 億美元。

以金融服務為導向的AI驅動型詐騙偵測利用機器學習、進階分析和行為監控等人工智慧技術，識別、預防和應對金融體系中的詐欺活動。這些解決方案即時分析大量交易和用戶數據，以偵測可能預示詐欺的異常模式和可疑行為。透過不斷從新數據中學習，AI驅動的系統能夠提高檢測準確率，減少徵兆，並幫助銀行、支付服務提供者和其他金融機構加強安全防護、最大限度地減少財務損失並提升客戶信任度。

數位交易激增和日益複雜的詐騙手段

數位銀行、電子商務和非接觸式支付的快速發展擴大了網路犯罪分子的攻擊面，詐騙手段也日益複雜。金融機構正面臨帳戶劫持、支付詐騙和身分盜竊激增的困境，因此，先進的偵測機制至關重要。人工智慧系統能夠以所需的速度和精度即時分析大量交易數據，識別人工處理或基於規則的系統可能遺漏的異常情況。隨著詐騙不斷利用人工智慧工具，金融業被迫部署具有同等智慧和適應性的防禦措施，以保護敏感的客戶資料和金融資產，這使得人工智慧成為現代安全基礎設施中不可或缺的組成部分。

資料整合實施成本高且複雜

實施人工智慧驅動的詐騙偵測系統需要前期在基礎設施、專業人員和持續的模型維護方面投入大量資金。許多金融機構，尤其是中小型銀行和金融科技公司，都難以應對將這些先進解決方案部署並整合到現有IT系統中的高昂成本。資料孤島和資料品質不一致進一步加劇了部署的複雜性，因為人工智慧模型需要龐大、乾淨且結構良好的資料集才能有效運作。此外，某些人工智慧演算法的「黑盒子」特性也為模型的可解釋性帶來了挑戰，使得金融機構難以滿足監管機構對其決策流程透明度和可解釋性的嚴格要求。

生成式人工智慧和圖神經網路的進展

生成式人工智慧 (GenAI) 和圖神經網路 (GNN) 等先進技術的出現，為詐騙偵測開啟了新的可能性。 GenAI 可用於模擬複雜的詐欺場景，從而進行穩健的模型訓練；而 GNN 則擅長揭示資料中隱藏的複雜關係和網路，使其在識別有組織的詐欺團夥和洗錢手段方面極為有效。這些技術有望顯著降低誤報率（誤報是營運的一大負擔），並提高威脅偵測的準確性。金融機構正日益尋求這些創新技術來增強其預測能力，而對於供應商而言，這為開發和部署下一代高度專業化的詐欺預防解決方案提供了機會。

不斷變化的監管環境和合規負擔

金融服務領域人工智慧的法規環境正在快速變化，為解決方案供應商和採用者帶來了不確定性和合規性風險。全球正在推出新的法規，重點關注人工智慧倫理、演算法課責和資料隱私，這要求系統不斷進行調整。未能遵守諸如GDPR、歐盟人工智慧法律或不斷演變的洗錢防制指令等標準，可能導致巨額罰款和聲譽損害。由於人工智慧模型旨在學習和適應，因此持續遵守不斷變化的法律體制仍然是一項挑戰。這造成了一個複雜的營運環境，在這個環境中，管治的彈性與技術能力同等重要。

新冠疫情的影響

新冠疫情是推動人工智慧詐騙偵測市場發展的關鍵催化劑。向數位化銀行和遠距辦公的快速大規模轉型導致線上交易激增，詐騙迅速利用這一趨勢，造成各類詐欺案件激增。這場危機迫使金融機構加快數位轉型步伐，並緊急部署由人工智慧驅動的安全解決方案以應對日益成長的風險。封鎖措施也凸顯了適用於遠端環境的自動化欺詐管理系統的必要性。後疫情時代，關注點已從危機應對轉向建立強大且擴充性的人工智慧架構，以適應以數位化為先的金融交易已成為常態的「新常態」。

在預測期內，支付詐騙領域預計將佔據最大的市場佔有率。

受全球數位支付交易量和交易額巨大影響，支付詐騙預計將佔據最大的市場佔有率。隨著消費者和企業擴大採用銀行卡、電子錢包和即時支付系統，這個管道已成為詐騙的主要目標。為了在欺詐性支付完成之前將其阻止，人工智慧即時監控交易並分析用戶行為的能力至關重要。

在預測期內，身分盜竊和帳戶劫持領域預計將呈現最高的複合年成長率。

在預測期內，身分盜竊和帳戶劫持領域預計將呈現最高的成長率。這主要是由於人員編制攻擊、網路釣魚詐騙和深度造假技術等手段的氾濫，這些手段被用來繞過傳統的安全措施。隨著金融服務日益向線上轉移，被盜數位身分的價值正在飆升。人工智慧解決方案，特別是那些利用生物識別、行為分析和無監督學習的解決方案，在檢測用戶行為中可能預示帳戶被盜用的細微異常方面，具有無可比擬的優勢。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率。這主要得益於主要技術供應商的存在、先進人工智慧解決方案的早期應用以及高度數位化的金融服務業。尤其值得一提的是，美國擁有健全的法規結構，強制執行嚴格的反詐欺措施，從而推動了持續的投資。消費者對數位安全的高度重視，以及各大銀行和金融科技公司對尖端詐騙偵測技術的大力投資，進一步鞏固了該地區的市場主導地位。

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、印度和東南亞等國家金融服務的快速數位化。大量沒有銀行帳戶的人群正在轉向直接使用行動銀行，從而形成了一個龐大的新型數位生態系統，同時也帶來了獨特的詐欺風險。各國政府在積極推動無現金經濟的同時，也實施需要強大安全基礎設施的數位身分計畫。金融科技產業的快速發展以及智慧型手機在該地區日益普及，使得針對行動優先環境量身定做的擴充性、人工智慧驅動的詐騙偵測解決方案的需求激增。

免費客製化服務：

訂閱本報告的用戶可享有以下免費自訂選項之一：

  • 公司簡介
    • 對其他公司（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣量身定做的主要國家/地區的市場估算、預測和複合年成長率（註：基於可行性檢查）
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 成長要素、挑戰與機遇
  • 競爭格局概述
  • 戰略考慮和建議

第2章：分析框架

  • 分析的目標和範圍
  • 相關人員分析
  • 分析的前提條件與限制
  • 分析方法

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
  • 科技與創新趨勢
  • 新興市場和高成長市場
  • 監管和政策環境
  • 感染疾病的影響及恢復前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商議價能力
    • 買方的議價能力
    • 替代產品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球金融服務業人工智慧詐騙偵測市場：按組件分類

  • 解決方案
    • 詐騙偵測平台
    • 數據監測和分析工具
    • 風險與合規管理
  • 服務
    • 專業服務
    • 託管服務

第6章：全球金融服務領域人工智慧驅動的詐騙偵測市場：按詐欺類型分類

  • 支付詐騙
  • 身分盜竊/帳戶劫持
  • 申請詐騙
  • 洗錢、反洗錢合規
  • 內部威脅
  • 其他類型的詐欺

第7章：全球金融服務領域人工智慧驅動的詐騙偵測市場：按技術分類

  • 機器學習（ML）
    • 監督式學習
    • 無監督學習
    • 強化學習
  • 深度學習（DL）
  • 自然語言處理（NLP）
  • 圖神經網路（GNN）
  • 生成式人工智慧（GenAI）

第8章：全球金融服務領域人工智慧驅動的詐騙偵測市場：按部署方式分類

  • 基於雲端的
  • 現場
  • 混合

第9章：全球金融服務領域人工智慧驅動的詐騙偵測市場：按應用分類

  • 即時交易監控
  • 客戶身份驗證（KYC）
  • 監理合規和報告
  • 風險評分和承保審查
  • 網路和網路安全監控
  • 其他用途

第10章：全球金融服務領域人工智慧驅動的詐騙偵測市場：按最終用戶分類

  • 銀行和金融機構
  • 支付服務供應商(PSP) 和金融科技公司
  • 保險公司
  • 電子商務與零售
  • 投資和證券公司
  • 政府/公共部門

第11章：全球金融服務業人工智慧驅動型詐騙偵測市場：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

  • 產業加值網路與供應鏈評估
  • 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

  • 企業合併（M&A）
  • 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

  • FICO
  • SAS Institute Inc.
  • NICE Actimize
  • BAE Systems
  • ACI Worldwide
  • IBM Corporation
  • Experian Information Solutions, Inc.
  • TransUnion LLC
  • Oracle Corporation
  • Microsoft Corporation
  • Google Cloud
  • Amazon Web Services, Inc.（AWS）
  • Feedzai
  • DataVisor
  • Featurespace
Product Code: SMRC34982

According to Stratistics MRC, the Global AI-Powered Fraud Detection in Financial Services Market is accounted for $6.3 billion in 2026 and is expected to reach $30.8 billion by 2034 growing at a CAGR of 21.9% during the forecast period. AI-Powered Fraud Detection in Financial Services is the application of artificial intelligence technologies, including machine learning, advanced analytics, and behavioral monitoring, to identify, prevent, and respond to fraudulent activities within financial systems. These solutions examine large volumes of transactional and user data in real time to detect unusual patterns and suspicious behavior that may signal fraud. By continuously learning from new data, AI-driven systems enhance detection accuracy, reduce false positives, and help banks, payment providers, and other financial institutions strengthen security, limit financial losses, and improve customer confidence.

Market Dynamics:

Driver:

Escalating digital transactions and sophisticated fraud schemes

The exponential growth of digital banking, e-commerce, and contactless payments has expanded the attack surface for cybercriminals, leading to increasingly sophisticated fraud schemes. Financial institutions are facing a surge in account takeovers, payment fraud, and identity theft, necessitating advanced detection mechanisms. AI-powered systems offer the speed and accuracy required to analyze high-volume transaction data in real-time, identifying anomalies that human-led or rule-based systems might miss. As fraudsters leverage their own AI tools, the financial sector is compelled to adopt equally intelligent, adaptive defenses to protect sensitive customer data and financial assets, making AI a critical component of modern security infrastructure.

Restraint:

High implementation costs and data integration complexities

The deployment of AI-powered fraud detection systems involves significant upfront investment in infrastructure, specialized talent, and ongoing model maintenance. Many financial institutions, particularly smaller banks and FinTechs, struggle with the high costs associated with acquiring and integrating these advanced solutions into legacy IT systems. Data silos and inconsistent data quality further complicate implementation, as AI models require vast, clean, and well-structured datasets to function effectively. Additionally, the "black box" nature of some AI algorithms can create challenges in model interpretability, making it difficult for institutions to meet stringent regulatory requirements for transparency and explainability in decision-making processes.

Opportunity:

Advancements in Generative AI and Graph Neural Networks

The emergence of advanced technologies like Generative AI (GenAI) and Graph Neural Networks (GNNs) is creating new frontiers in fraud detection. GenAI can be used to simulate sophisticated fraud scenarios for robust model training, while GNNs excel at uncovering hidden, complex relationships and networks within data, making them highly effective at identifying organized fraud rings and money laundering schemes. These technologies offer the potential to significantly reduce false positives, which are a major operational burden, and improve the accuracy of threat detection. Financial institutions are increasingly exploring these innovations to gain a predictive edge, offering vendors opportunities to develop and deploy next-generation, highly specialized anti-fraud solutions.

Threat:

Evolving regulatory landscape and compliance burden

The regulatory environment for AI in financial services is rapidly evolving, creating uncertainty and compliance risks for solution providers and adopters. New regulations focusing on AI ethics, algorithmic accountability, and data privacy are being introduced globally, requiring constant system adjustments. Failure to comply with standards like GDPR, the EU's AI Act, or evolving anti-money laundering (AML) directives can result in substantial fines and reputational damage. As AI models are designed to learn and adapt, ensuring they remain compliant with shifting legal frameworks is a persistent challenge. This creates a complex operational environment where agility in governance is as crucial as technological capability.

Covid-19 Impact

The COVID-19 pandemic acted as a significant catalyst for the AI-powered fraud detection market. The sudden, massive shift to digital banking and remote work created a surge in online transactions, which fraudsters quickly exploited, leading to a spike in various fraud types. This urgency forced financial institutions to accelerate their digital transformation initiatives and fast-track the adoption of AI-driven security solutions to manage the increased risk. Lockdowns also highlighted the need for automated, remote-friendly fraud management systems. Post-pandemic, the focus has shifted from crisis response to building resilient, scalable AI architectures capable of handling the new normal of persistent digital-first financial interactions.

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

The payment fraud segment is expected to account for the largest market share, driven by the sheer volume and value of digital payments processed globally. As consumers and businesses increasingly adopt cards, digital wallets, and real-time payment systems, this channel becomes the primary target for fraudsters. AI's ability to perform real-time transaction monitoring and behavioral analytics is essential for intercepting unauthorized payments before completion.

The identity theft & account takeover segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the identity theft and account takeover segment is predicted to witness the highest growth rate. This is fueled by the proliferation of credential-stuffing attacks, phishing schemes, and deepfake technology used to bypass traditional security measures. As more financial services migrate online, the value of stolen digital identities has skyrocketed. AI-powered solutions, particularly those utilizing biometrics, behavioral analytics, and unsupervised learning, are uniquely effective at detecting subtle anomalies in user behavior indicative of account compromise.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the presence of major technology vendors, early adoption of advanced AI solutions, and a highly digitized financial services sector. The United States, in particular, has a robust regulatory framework that mandates stringent fraud prevention measures, fueling continuous investment. High consumer awareness of digital security and the concentration of leading banks and FinTech companies investing heavily in cutting-edge fraud detection technologies further solidify the region's market dominance.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapid digitalization of financial services in countries like China, India, and Southeast Asia. A massive unbanked population is leapfrogging directly to mobile banking, creating a vast new digital ecosystem with inherent fraud risks. Governments are actively promoting cashless economies while implementing digital identity programs, which necessitates robust security infrastructure. The region's burgeoning FinTech scene and increasing smartphone penetration are creating immense demand for scalable, AI-powered fraud detection solutions tailored to mobile-first environments.

Key players in the market

Some of the key players in AI-Powered Fraud Detection in Financial Services Market include FICO, SAS Institute Inc., NICE Actimize, BAE Systems, ACI Worldwide, IBM Corporation, Experian Information Solutions, Inc., TransUnion LLC, Oracle Corporation, Microsoft Corporation, Google Cloud, Amazon Web Services, Inc., Feedzai, DataVisor, and Featurespace.

Key Developments:

In March 2026, IBM completed its acquisition of Confluent, Inc., the data streaming platform that more than 6,500 enterprises, including 40% of the Fortune 500, rely on to power real-time operations. Together, IBM and Confluent deliver a smart data platform that gives every AI model, agent, and automated workflow the real-time, trusted data needed to operate across on-premises and hybrid cloud environments at scale.

In February 2026, Oracle and Oracle Red Bull Racing announced a multi-year extension and expansion of their title partnership as the Team prepares for the most significant regulation shift in modern F1 history. This renewal builds on the most integrated team technology partnership in F1, with Oracle technology powering the Team's success and helping deliver a competitive advantage under pressure.

Components Covered:

  • Solutions
  • Services

Fraud Types Covered:

  • Payment Fraud
  • Identity Theft & Account Takeover
  • Application Fraud
  • Money Laundering & Anti-Money Laundering (AML) Compliance
  • Insider Threats
  • Other Fraud Types

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Graph Neural Networks (GNN)
  • Generative AI (GenAI)

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

Applications Covered:

  • Real-time Transaction Monitoring
  • Customer Identity Verification (KYC)
  • Regulatory Compliance & Reporting
  • Risk Scoring & Underwriting
  • Network & Cybersecurity Monitoring
  • Other Applications

End Users Covered:

  • Banks & Financial Institutions
  • Payment Service Providers (PSPs) & FinTechs
  • Insurance Companies
  • E-commerce & Retail
  • Investment & Securities Firms
  • Government & Public Sector

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 in Financial Services Market, By Component

  • 5.1 Solutions
    • 5.1.1 Fraud Detection Platforms
    • 5.1.2 Data Monitoring & Analytics Tools
    • 5.1.3 Risk & Compliance Management
  • 5.2 Services
    • 5.2.1 Professional Services
    • 5.2.2 Managed Services

6 Global AI-Powered Fraud Detection in Financial Services Market, By Fraud Type

  • 6.1 Payment Fraud
  • 6.2 Identity Theft & Account Takeover
  • 6.3 Application Fraud
  • 6.4 Money Laundering & Anti-Money Laundering (AML) Compliance
  • 6.5 Insider Threats
  • 6.6 Other Fraud Types

7 Global AI-Powered Fraud Detection in Financial Services Market, By Technology

  • 7.1 Machine Learning (ML)
    • 7.1.1 Supervised Learning
    • 7.1.2 Unsupervised Learning
    • 7.1.3 Reinforcement Learning
  • 7.2 Deep Learning
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Graph Neural Networks (GNN)
  • 7.5 Generative AI (GenAI)

8 Global AI-Powered Fraud Detection in Financial Services Market, By Deployment Mode

  • 8.1 Cloud-Based
  • 8.2 On-Premises
  • 8.3 Hybrid

9 Global AI-Powered Fraud Detection in Financial Services Market, By Application

  • 9.1 Real-time Transaction Monitoring
  • 9.2 Customer Identity Verification (KYC)
  • 9.3 Regulatory Compliance & Reporting
  • 9.4 Risk Scoring & Underwriting
  • 9.5 Network & Cybersecurity Monitoring
  • 9.6 Other Applications

10 Global AI-Powered Fraud Detection in Financial Services Market, By End User

  • 10.1 Banks & Financial Institutions
  • 10.2 Payment Service Providers (PSPs) & FinTechs
  • 10.3 Insurance Companies
  • 10.4 E-commerce & Retail
  • 10.5 Investment & Securities Firms
  • 10.6 Government & Public Sector

11 Global AI-Powered Fraud Detection in Financial Services 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 FICO
  • 14.2 SAS Institute Inc.
  • 14.3 NICE Actimize
  • 14.4 BAE Systems
  • 14.5 ACI Worldwide
  • 14.6 IBM Corporation
  • 14.7 Experian Information Solutions, Inc.
  • 14.8 TransUnion LLC
  • 14.9 Oracle Corporation
  • 14.10 Microsoft Corporation
  • 14.11 Google Cloud
  • 14.12 Amazon Web Services, Inc. (AWS)
  • 14.13 Feedzai
  • 14.14 DataVisor
  • 14.15 Featurespace

List of Tables

  • Table 1 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Solutions (2023-2034) ($MN)
  • Table 4 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Fraud Detection Platforms (2023-2034) ($MN)
  • Table 5 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Data Monitoring & Analytics Tools (2023-2034) ($MN)
  • Table 6 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Risk & Compliance Management (2023-2034) ($MN)
  • Table 7 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Services (2023-2034) ($MN)
  • Table 8 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Professional Services (2023-2034) ($MN)
  • Table 9 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 10 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Fraud Type (2023-2034) ($MN)
  • Table 11 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Payment Fraud (2023-2034) ($MN)
  • Table 12 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Identity Theft & Account Takeover (2023-2034) ($MN)
  • Table 13 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Application Fraud (2023-2034) ($MN)
  • Table 14 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Money Laundering & Anti-Money Laundering (AML) Compliance (2023-2034) ($MN)
  • Table 15 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Insider Threats (2023-2034) ($MN)
  • Table 16 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Other Fraud Types (2023-2034) ($MN)
  • Table 17 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Technology (2023-2034) ($MN)
  • Table 18 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 19 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Supervised Learning (2023-2034) ($MN)
  • Table 20 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Unsupervised Learning (2023-2034) ($MN)
  • Table 21 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 22 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 23 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 24 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Graph Neural Networks (GNN) (2023-2034) ($MN)
  • Table 25 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Generative AI (GenAI) (2023-2034) ($MN)
  • Table 26 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 27 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 28 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 29 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 30 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Application (2023-2034) ($MN)
  • Table 31 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Real-time Transaction Monitoring (2023-2034) ($MN)
  • Table 32 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Customer Identity Verification (KYC) (2023-2034) ($MN)
  • Table 33 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Regulatory Compliance & Reporting (2023-2034) ($MN)
  • Table 34 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Risk Scoring & Underwriting (2023-2034) ($MN)
  • Table 35 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Network & Cybersecurity Monitoring (2023-2034) ($MN)
  • Table 36 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 37 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By End User (2023-2034) ($MN)
  • Table 38 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Banks & Financial Institutions (2023-2034) ($MN)
  • Table 39 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Payment Service Providers (PSPs) & FinTechs (2023-2034) ($MN)
  • Table 40 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Insurance Companies (2023-2034) ($MN)
  • Table 41 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By E-commerce & Retail (2023-2034) ($MN)
  • Table 42 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Investment & Securities Firms (2023-2034) ($MN)
  • Table 43 Global AI-Powered Fraud Detection in Financial Services Market Outlook, By Government & Public Sector (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.