查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
2021727

以金融服务为导向的AI驱动型诈骗侦测市场：预测（至2034年）－按组件、诈欺类型、技术、部署方式、应用、最终用户和地区进行分析

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个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球金融服务领域人工智慧驱动的诈骗侦测市场规模将达到 63 亿美元，并在预测期内以 21.9% 的复合年增长率增长，到 2034 年将达到 308 亿美元。

以金融服务为导向的AI驱动型诈骗侦测利用机器学习、进阶分析和行为监控等人工智慧技术，识别、预防和应对金融体系中的诈欺活动。这些解决方案即时分析大量交易和用户数据，以侦测可能预示诈欺的异常模式和可疑行为。透过不断从新数据中学习，AI驱动的系统能够提高检测准确率，减少征兆，并帮助银行、支付服务提供者和其他金融机构加强安全防护、最大限度地减少财务损失并提升客户信任度。

数位交易激增和日益复杂的诈骗手段

数位银行、电子商务和非接触式支付的快速发展扩大了网路犯罪分子的攻击面，诈骗手段也日益复杂。金融机构正面临帐户劫持、支付诈骗和身分盗窃激增的困境，因此，先进的侦测机制至关重要。人工智慧系统能够以所需的速度和精度即时分析大量交易数据，识别人工处理或基于规则的系统可能遗漏的异常情况。随着诈骗不断利用人工智慧工具，金融业被迫部署具有同等智慧和适应性的防御措施，以保护敏感的客户资料和金融资产，这使得人工智慧成为现代安全基础设施中不可或缺的组成部分。

资料整合实施成本高且复杂

实施人工智慧驱动的诈骗侦测系统需要前期在基础设施、专业人员和持续的模型维护方面投入大量资金。许多金融机构，尤其是中小型银行和金融科技公司，都难以应对将这些先进解决方案部署并整合到现有IT系统中的高昂成本。资料孤岛和资料品质不一致进一步加剧了部署的复杂性，因为人工智慧模型需要庞大、干净且结构良好的资料集才能有效运作。此外，某些人工智慧演算法的「黑盒子」特性也为模型的可解释性带来了挑战，使得金融机构难以满足监管机构对其决策流程透明度和可解释性的严格要求。

生成式人工智慧和图神经网路的进展

生成式人工智慧 (GenAI) 和图神经网路 (GNN) 等先进技术的出现，为诈骗侦测开启了新的可能性。 GenAI 可用于模拟复杂的诈欺场景，从而进行稳健的模型训练；而 GNN 则擅长揭示资料中隐藏的复杂关係和网络，使其在识别有组织的诈欺团伙和洗钱手段方面极为有效。这些技术有望显着降低误报率（误报是营运的一大负担），并提高威胁侦测的准确性。金融机构正日益寻求这些创新技术来增强其预测能力，而对于供应商而言，这为开发和部署下一代高度专业化的诈欺预防解决方案提供了机会。

不断变化的监管环境和合规负担

金融服务领域人工智慧的法规环境正在快速变化，为解决方案供应商和采用者带来了不确定性和合规性风险。全球正在推出新的法规，重点关注人工智慧伦理、演算法课责和资料隐私，这要求系统不断进行调整。未能遵守诸如GDPR、欧盟人工智慧法律或不断演变的洗钱防制指令等标准，可能导致巨额罚款和声誉损害。由于人工智慧模型旨在学习和适应，因此持续遵守不断变化的法律体制仍然是一项挑战。这造成了一个复杂的营运环境，在这个环境中，管治的弹性与技术能力同等重要。

新冠疫情的影响

新冠疫情是推动人工智慧诈骗侦测市场发展的关键催化剂。向数位化银行和远距办公的快速大规模转型导致线上交易激增，诈骗迅速利用这一趋势，造成各类诈欺案件激增。这场危机迫使金融机构加快数位转型步伐，并紧急部署由人工智慧驱动的安全解决方案以应对日益增长的风险。封锁措施也凸显了适用于远端环境的自动化欺诈管理系统的必要性。后疫情时代，关注点已从危机应对转向建立强大且扩充性的人工智慧架构，以适应以数位化为先的金融交易已成为常态的「新常态」。

在预测期内，支付诈骗领域预计将占据最大的市场份额。

受全球数位支付交易量和交易额巨大影响，支付诈骗预计将占据最大的市场份额。随着消费者和企业越来越多地采用银行卡、电子钱包和即时支付系统，这个管道已成为诈骗的主要目标。为了在欺诈性支付完成之前将其阻止，人工智慧即时监控交易并分析用户行为的能力至关重要。

在预测期内，身分盗窃和帐户劫持领域预计将呈现最高的复合年增长率。

在预测期内，身分盗窃和帐户劫持领域预计将呈现最高的成长率。这主要是由于人员编制攻击、网路钓鱼诈骗和深度造假技术等手段的氾滥，这些手段被用来绕过传统的安全措施。随着金融服务日益向线上转移，被盗数位身分的价值正在飙升。人工智慧解决方案，特别是那些利用生物识别、行为分析和无监督学习的解决方案，在检测用户行为中可能预示帐户被盗用的细微异常方面，具有无可比拟的优势。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额。这主要得益于主要技术供应商的存在、先进人工智慧解决方案的早期应用以及高度数位化的金融服务业。尤其值得一提的是，美国拥有健全的法规结构，强制执行严格的反诈欺措施，从而推动了持续的投资。消费者对数位安全的高度重视，以及各大银行和金融科技公司对尖端诈骗侦测技术的大力投资，进一步巩固了该地区的市场主导地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于中国、印度和东南亚等国家金融服务的快速数位化。大量没有银行帐户的人群正在转向直接使用行动银行，从而形成了一个庞大的新型数位生态系统，同时也带来了独特的诈欺风险。各国政府在积极推动无现金经济的同时，也实施需要强大安全基础设施的数位身分计画。金融科技产业的快速发展以及智慧型手机在该地区日益普及，使得针对行动优先环境量身定制的扩充性、人工智慧驱动的诈骗侦测解决方案的需求激增。

免费客製化服务：

订阅本报告的用户可享有以下免费自订选项之一：

  • 公司简介
    • 对其他公司（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣量身定制的主要国家/地区的市场估算、预测和复合年增长率（註：基于可行性检查）
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 成长要素、挑战与机会
  • 竞争格局概述
  • 战略考虑和建议

第二章：分析框架

  • 分析的目标和范围
  • 相关人员分析
  • 分析的前提条件与限制
  • 分析方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 科技与创新趋势
  • 新兴市场和高成长市场
  • 监管和政策环境
  • 感染疾病的影响及恢復前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商议价能力
    • 买方的议价能力
    • 替代产品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球金融服务业人工智慧诈骗侦测市场：按组件划分

  • 解决方案
    • 诈骗侦测平台
    • 数据监测和分析工具
    • 风险与合规管理
  • 服务
    • 专业服务
    • 託管服务

第六章：全球金融服务领域人工智慧驱动的诈骗侦测市场：按诈欺类型划分

  • 支付诈骗
  • 身分盗窃/帐户劫持
  • 申请诈骗
  • 洗钱、反洗钱合规
  • 内部威胁
  • 其他类型的诈欺

第七章：全球金融服务领域人工智慧驱动的诈骗侦测市场：按技术划分

  • 机器学习（ML）
    • 监督式学习
    • 无监督学习
    • 强化学习
  • 深度学习（DL）
  • 自然语言处理（NLP）
  • 图神经网路（GNN）
  • 生成式人工智慧（GenAI）

第八章：全球金融服务领域人工智慧驱动的诈骗侦测市场：按部署方式划分

  • 基于云端的
  • 现场
  • 杂交种

第九章：全球金融服务领域人工智慧驱动的诈骗侦测市场：按应用划分

  • 即时交易监控
  • 客户身份验证（KYC）
  • 监理合规和报告
  • 风险评分和承保审查
  • 网路和网路安全监控
  • 其他用途

第十章：全球金融服务领域人工智慧驱动的诈骗侦测市场：按最终用户划分

  • 银行和金融机构
  • 支付服务供应商(PSP) 和金融科技公司
  • 保险公司
  • 电子商务与零售
  • 投资和证券公司
  • 政府/公共部门

第十一章：全球金融服务业人工智慧驱动型诈骗侦测市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十二章 策略市场资讯

  • 产业加值网络与供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十三章 产业趋势与策略倡议

  • 企业合併（M&A）
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十四章：公司简介

  • 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.