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市场调查报告书
商品编码
1937290

金融科技领域人工智慧市场占有率分析、产业趋势与统计、成长预测（2026-2031）

AI In Fintech - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3个工作天内

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简介目录

2025年，金融科技领域人工智慧的市场规模为300亿美元，预计到2031年将达到990.9亿美元，高于2026年的366.1亿美元。

预测期（2026-2031 年）的复合年增长率预计为 22.04%。

人工智慧在金融科技市场的应用-IMG1

成长的驱动力来自开放银行政策（该政策释放了分散的客户资料）、日趋成熟的即时支付基础设施以及云端原生人工智慧平台（该平台降低了中型银行的营运成本）。生成式人工智慧助理（Generative AI Copilot）将模型风险管理週期从数月缩短至数天，使金融机构能够以前所未有的速度发布合规的风险模型。来自纽约梅隆银行等金融机构的每月超过9兆美元的高频支付数据正被输入到人工智慧引擎中，从而增强诈欺检测和流动性预测能力。这些因素的整合正在形成一个良性循环：更低的总体拥有成本推动了更广泛的应用，而应用的广泛又反过来创造了更丰富的数据集并提高了模型的准确性。

全球人工智慧市场趋势及金融科技洞察

开放银行监理加速人工智慧主导的流程自动化

诸如PSD3（第三版支付服务指示）之类的资料共用指示赋予人工智慧引擎对多家金融机构记录进行一致且经授权的存取的能力，从而实现即时信用评分和高度个人化的优惠。随着PSD3于2024年生效，欧洲各银行正基于API优先架构重新设计产品开发流程，将先前孤立的资料集输入机器学习模型。中型金融机构正透过将合规投资转化为创新催化剂，并将监管成本转化为收入成长，以提升自身竞争力。开放银行采用率超过87%的金融机构市场，人工智慧服务的渗透率已显着提高。

即时支付资料流的爆炸性成长

VisaNet+AI 对每笔核准交易的预测准确率高达 98%，而智慧结算预测则透过增加七天现金流预测来减少流动性缓衝。即时付款基础能够提供批次处理系统无法捕捉的行为讯号，使人工智慧能够在诈骗活动发生后的几毫秒内侦测到它们。研究表明，94% 的支付专业人士认为人工智慧对于预防诈欺至关重要，77% 的消费者希望他们的金融机构采用人工智慧。纽约梅隆银行已实现了 90% 的支付指令处理自动化，使分析师能够专注于增值任务。即时数据可用性还支援基于动态现金流指标的即时信贷决策。

特定领域人工智慧人才短缺

对具备机器学习技能和监管知识的专家的需求是供应量的2-4倍，74%的雇主表示招聘面临挑战。仅有25%的欧洲银行拥有正式的生成式人工智慧培训项目，加剧了人才缺口。相关职位的薪资比传统金融职位高出40-60%，使得科技巨头和顶级银行更青睐这类人才。中型企业面临人才短缺的风险，这将导致计划工期延长、成本上升，并阻碍人工智慧技术的应用。

细分市场分析

预计到2025年，解决方案领域市场规模将达到214.4亿美元，占金融科技人工智慧市场71.45%的份额。企业倾向于选择能够将诈欺分析、客户支援和管治整合到单一管理平台中的方案。 FICO基于区块链的管治套件荣获2025年创新奖，充分展现了整合解决方案为何正成为主流。目前规模较小的服务领域预计到2031年将以27.95%的复合年增长率增长，因为银行正在寻求咨询合作伙伴，以帮助他们建立复杂的生成式人工智慧流程并管理每天收到的234份监管通知。

顾问公司正在将合规义务转化为模型设计，从而加快价值实现速度。这种需求使专业的系统整合商业务繁忙，服务费也逐渐成为可预测的收入来源。服务专业知识的普及吸引了先前因缺乏内部技能而推迟采用人工智慧的中型企业，从而扩大了金融科技领域的人工智慧基本客群。

到2025年，云端环境将占部署收入的81.35%，这主要得益于弹性运算能够处理大量交易。摩根大通的架构将70%的应用程式部署在公共云端上，而敏感工作负载则託管在一个价值20亿美元的私有设施中。随着监管机构收紧居住规则，以及银行寻求降低单一供应商故障的风险，混合部署预计将以27.4%的复合年增长率成长。

混合模式兼具两者的优势：一方面，训练流程保留在本地以确保资料主权；另一方面，推理处理则在云端进行。这种柔软性使混合模型成为一种永续的选择，尤其适用于资料本地化执行严格的司法管辖区。

《金融科技领域的人工智慧》报告按类型（解决方案和服务）、部署模式（云端和本地部署）、用例（诈欺和风险管理、聊天机器人和虚拟助理等）、组织规模（大型企业、中小企业、新型银行）、最终用户（零售银行、保险等）和地区进行细分。

区域分析

预计到2025年，北美将占据37.60%的收入份额，这得益于其成熟的金融基础设施和清晰但分散的监管指导。摩根大通拥有2,000名人工智慧专家和超过400个应用案例，展现了其在本地的深厚技术实力。加拿大Neo Financial等新兴银行正将人工智慧扩展到服务不足的领域，而墨西哥则在利用人工智慧促进普惠金融。持续的公共和私人投资使北美成为创新试验场，并将全球最佳实践引入金融科技人工智慧市场。

预计到2031年，亚太地区将以33.1%的复合年增长率实现最快成长。中国将在2024年投资21亿美元用于生成式人工智慧，企业采用率将达83%，远超西方国家的渗透率。印度和日本正透过依赖人工智慧引擎的综合贷款和量化交易平台迅速发展。该地区的金融科技收入预计将从2021年的2,450亿美元成长到2030年的1.5兆美元，87%的银行计划与金融科技公司建立合作关係。新加坡是行动支付领域的主导，而澳洲和纽西兰预计将获得与其GDP不成比例的人工智慧价值份额。

合规负担正在减缓欧洲人工智慧的普及速度。欧盟人工智慧立法引入了风险分级体系，虽然增加了管治成本，但确保了其合乎伦理的应用。英国的生成式人工智慧应用率已达70%，并正利用脱欧后的弹性为银行客製化沙盒环境。德国和法国正在其主要企业内部建立人工智慧卓越中心，北欧国家则在试行绿色金融评估框架。东欧市场正尝试将人工智慧应用于跨境工资汇款，从而重塑传统的服务边界。

其他福利：

  • Excel格式的市场预测（ME）表
  • 3个月的分析师支持

目录

第一章 引言

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章 市场情势

  • 市场概览
  • 市场驱动因素
    • 开放银行政策将加速人工智慧主导的流程自动化。
    • 即时支付资料流的激增
    • 云端原生人工智慧平台降低中型金融机构的总拥有成本
    • 生成式人工智慧助理显着缩短了模型风险管理週期时间
    • 人工智慧驱动的ESG评分为绿色金融释放奖励
  • 市场限制
    • 特定领域人工智慧人才短缺
    • 人工智慧模型管治方面的监管指导分散
    • GPU供应链波动性加剧，推高推理成本。
  • 价值链分析
  • 监管环境
  • 技术展望
  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 宏观经济因素如何影响市场

第五章 市场规模与成长预测

  • 按组件
    • 解决方案
    • 服务
  • 透过部署模式
    • 云
    • 本地部署
  • 透过使用
    • 诈欺和风险管理
    • 聊天机器人和虚拟助手
    • 信用评分和信用筛选
    • 量化管理和资产管理
    • 监理科技与合规分析
    • 其他的
  • 按组织规模
    • 大公司
    • 小型企业和新型银行
  • 最终用户
    • 零售银行
    • 保险
    • 投资和资产管理
    • 支付和汇款服务供应商
    • 其他的
  • 按地区
    • 北美洲
      • 我们
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 智利
      • 南美洲其他地区
    • 欧洲
      • 德国
      • 英国
      • 法国
      • 义大利
      • 西班牙
      • 其他欧洲
    • 亚太地区
      • 中国
      • 印度
      • 日本
      • 韩国
      • 马来西亚
      • 新加坡
      • 澳洲
      • 亚太其他地区
    • 中东和非洲
      • 中东
        • 阿拉伯聯合大公国
        • 沙乌地阿拉伯
        • 土耳其
        • 其他中东地区
      • 非洲
        • 南非
        • 奈及利亚
        • 其他非洲地区

第六章 竞争情势

  • 市场集中度
  • 策略趋势
  • 市占率分析
  • 公司简介
    • Amazon Web Services, Inc.
    • Microsoft Corporation
    • International Business Machines Corporation
    • Google LLC（Alphabet Inc.）
    • Fair Isaac Corporation
    • SAS Institute Inc.
    • Intel Corporation
    • NVIDIA Corporation
    • Salesforce, Inc.
    • Stripe, Inc.
    • Plaid Inc.
    • ComplyAdvantage Ltd.
    • Onfido Ltd.
    • Ripple Labs Inc.
    • DataRobot, Inc.
    • Upstart Holdings, Inc.
    • ZestFinance, Inc.
    • Darktrace Limited
    • Truera, Inc.
    • Active Intelligence Pte Ltd.

第七章 市场机会与未来展望

简介目录
Product Code: 61424

The AI in Fintech market was valued at USD 30 billion in 2025 and estimated to grow from USD 36.61 billion in 2026 to reach USD 99.09 billion by 2031, at a CAGR of 22.04% during the forecast period (2026-2031).

AI In Fintech - Market - IMG1

Growth is being propelled by open-banking mandates that liberate granular customer data, the maturation of real-time payment rails, and cloud-native AI platforms that trim operating costs for mid-tier banks. Generative AI copilots are compressing model-risk-management timelines from months to days, letting institutions release compliant risk models at unprecedented speed. High-frequency payment data, more than USD 9 trillion monthly at institutions such as BNY Mellon, feeds AI engines that sharpen fraud detection and liquidity forecasts. Convergence of these forces sustains a flywheel in which lower total cost of ownership invites wider adoption, and wider adoption produces richer datasets that reinforce model accuracy.

Global AI In Fintech Market Trends and Insights

Open Banking Mandates Accelerating AI-Led Process Automation

Mandatory data-sharing rules such as PSD3 grant AI engines consistent, permissioned access to multi-institution bank records, enabling real-time credit scoring and hyper-personalized offers. PSD3 went live in 2024, prompting European banks to redesign product origination workflows around API-first architectures that feed machine-learning models with previously siloed datasets. Mid-tier institutions gain competitive parity because compliance investments double as innovation enablers, turning regulatory cost into revenue growth levers. Markets where open-banking adoption exceeds 87% of institutions already display elevated AI service penetration.

Explosion of Real-Time Payments Data Streams

VisaNet +AI processes each authorization with 98% stability prediction accuracy, while its Smarter Settlement Forecast adds seven-day cash-flow projections that shrink liquidity buffers. Real-time rails broadcast behavioral signals that batch systems miss, letting AI flag fraud milliseconds after initiation . Surveys show 94% of payments professionals view AI as indispensable for fraud mitigation, and 77% of consumers expect institutions to deploy it. BNY Mellon automated 90% of back-office payment instruction handling, freeing analysts for value-added tasks. Instant data availability also powers live credit decisions based on dynamic cash-flow metrics.

Shortage of Domain-Specific AI Talent

Demand for professionals who blend machine-learning mastery with regulatory fluency exceeds supply by 2-4 times, with 74% of employers reporting hiring struggles. European banks note that only 25% have formal GenAI training pipelines, widening capability gaps. Salary premiums of 40-60% over traditional finance roles tilt the playing field toward tech giants and tier-one banks. Mid-tier firms risk stalled deployments as talent scarcity inflates project timelines and costs.

Other drivers and restraints analyzed in the detailed report include:

  1. Cloud-Native AI Platforms Lowering TCO for Mid-Tier Financial Institutions
  2. GenAI Copilots Slashing Model-Risk-Management Cycle Times
  3. Fragmented Regulatory Guidance on AI Model Governance

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Solutions generated USD 21.44 billion in 2025, equal to 71.45% of the AI in Fintech market. Enterprises favor platforms that unify fraud analytics, customer support, and governance within a single control plane. FICO's blockchain-enabled governance suite, which won a 2025 innovation award, illustrates why integrated offerings dominate. The services segment is smaller today but is projected to grow at 27.95% CAGR through 2031 as banks seek advisory partners to configure complex GenAI pipelines and manage the daily swell of 234 regulatory notices.

Consultancies help translate compliance obligations into model design, accelerating time to value. This demand keeps specialized system integrators busy and cements service fees as a predictable revenue stream. As service expertise proliferates, mid-tier firms that once delayed AI adoption due to limited internal skill sets now jump in, broadening the AI in Fintech market customer base.

Cloud environments delivered 81.35% of deployment revenues in 2025 on the back of elastic compute that processes massive transaction volumes. JPMorgan Chase's architecture shows 70% of applications in public cloud while sensitive workloads reside in USD 2 billion private facilities. Hybrid deployments are forecast to advance at 27.4% CAGR as regulators tighten residency rules and banks look to limit exposure to single-vendor outages.

Hybrid models place training pipelines on-premise for sovereignty yet run inference in cloud, unlocking the best of both worlds. This flexibility positions hybrid as a durable choice, particularly in jurisdictions enforcing strict data localization.

The AI in Fintech Market Report is Segmented by Type (Solutions and Services), Deployment (Cloud and On-Premise), Application (Fraud and Risk Management, Chatbots and Virtual Assistants, and More), Organization Size (Large Enterprises and SMEs and Neo-Banks), End-User (Retail Banking, Insurance, and More), and Geography.

Geography Analysis

North America held 37.60% revenue share in 2025, supported by a mature financial stack and clear though fragmented regulatory guidance. JPMorgan Chase fields 2,000 AI specialists and over 400 live use cases, underscoring local skill depth. Canada's challenger banks such as Neo Financial scale AI to underserved segments, and Mexico leverages AI for financial inclusion. Continued public-private investment sustains North America as an innovation laboratory, feeding global best practices back into the AI in Fintech market.

Asia-Pacific is projected to register the fastest 33.1% CAGR through 2031. China poured USD 2.1 billion into generative AI in 2024 and records 83% enterprise usage, dwarfing western penetration rates. India and Japan extend momentum through inclusive credit and quantitative trading desks that rely on AI engines. The region's fintech revenue could move from USD 245 billion in 2021 to USD 1.5 trillion by 2030, with 87% of banks planning fintech partnerships. Singapore leads in mobile payments, while Australia and New Zealand expect disproportionate AI value capture relative to GDP.

Europe demonstrates strong adoption tempered by compliance overhead. The EU AI Act imposes a risk-tier system that elevates governance costs but assures ethical deployment. The UK reports 70% GenAI usage, leveraging post-Brexit agility to tailor banking sandboxes. Germany and France fund AI centers of excellence inside national champions, and the Nordics pilot green-finance scoring frameworks. Eastern markets experiment with AI for cross-border wage remittances, redrawing traditional service boundaries.

  1. Amazon Web Services, Inc.
  2. Microsoft Corporation
  3. International Business Machines Corporation
  4. Google LLC (Alphabet Inc.)
  5. Fair Isaac Corporation
  6. SAS Institute Inc.
  7. Intel Corporation
  8. NVIDIA Corporation
  9. Salesforce, Inc.
  10. Stripe, Inc.
  11. Plaid Inc.
  12. ComplyAdvantage Ltd.
  13. Onfido Ltd.
  14. Ripple Labs Inc.
  15. DataRobot, Inc.
  16. Upstart Holdings, Inc.
  17. ZestFinance, Inc.
  18. Darktrace Limited
  19. Truera, Inc.
  20. Active Intelligence Pte Ltd.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Open banking mandates accelerating AI-led process automation
    • 4.2.2 Explosion of real-time payments data streams
    • 4.2.3 Cloud-native AI platforms lowering TCO for mid-tier FIs
    • 4.2.4 GenAI copilots slashing model-risk-management cycle-times
    • 4.2.5 AI-powered ESG scoring unlocking green-finance incentives
  • 4.3 Market Restraints
    • 4.3.1 Shortage of domain-specific AI talent
    • 4.3.2 Fragmented regulatory guidance on AI model governance
    • 4.3.3 Rising GPU supply-chain volatility inflating inference costs
  • 4.4 Value Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Bargaining Power of Suppliers
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Threat of New Entrants
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Intensity of Competitive Rivalry
  • 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Component
    • 5.1.1 Solutions
    • 5.1.2 Services
  • 5.2 By Deployment Mode
    • 5.2.1 Cloud
    • 5.2.2 On-premise
  • 5.3 By Application
    • 5.3.1 Fraud and Risk Management
    • 5.3.2 Chatbots and Virtual Assistants
    • 5.3.3 Credit Scoring and Underwriting
    • 5.3.4 Quantitative and Asset Management
    • 5.3.5 RegTech and Compliance Analytics
    • 5.3.6 Others
  • 5.4 By Organization Size
    • 5.4.1 Large Enterprises
    • 5.4.2 SMEs and Neo-banks
  • 5.5 By End-user
    • 5.5.1 Retail Banking
    • 5.5.2 Insurance
    • 5.5.3 Investment and Wealth Management
    • 5.5.4 Payments and Remittances Providers
    • 5.5.5 Others
  • 5.6 By Geography
    • 5.6.1 North America
      • 5.6.1.1 United States
      • 5.6.1.2 Canada
      • 5.6.1.3 Mexico
    • 5.6.2 South America
      • 5.6.2.1 Brazil
      • 5.6.2.2 Argentina
      • 5.6.2.3 Chile
      • 5.6.2.4 Rest of South America
    • 5.6.3 Europe
      • 5.6.3.1 Germany
      • 5.6.3.2 United Kingdom
      • 5.6.3.3 France
      • 5.6.3.4 Italy
      • 5.6.3.5 Spain
      • 5.6.3.6 Rest of Europe
    • 5.6.4 Asia-Pacific
      • 5.6.4.1 China
      • 5.6.4.2 India
      • 5.6.4.3 Japan
      • 5.6.4.4 South Korea
      • 5.6.4.5 Malaysia
      • 5.6.4.6 Singapore
      • 5.6.4.7 Australia
      • 5.6.4.8 Rest of Asia-Pacific
    • 5.6.5 Middle East and Africa
      • 5.6.5.1 Middle East
        • 5.6.5.1.1 United Arab Emirates
        • 5.6.5.1.2 Saudi Arabia
        • 5.6.5.1.3 Turkey
        • 5.6.5.1.4 Rest of Middle East
      • 5.6.5.2 Africa
        • 5.6.5.2.1 South Africa
        • 5.6.5.2.2 Nigeria
        • 5.6.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products and Services, and Recent Developments)
    • 6.4.1 Amazon Web Services, Inc.
    • 6.4.2 Microsoft Corporation
    • 6.4.3 International Business Machines Corporation
    • 6.4.4 Google LLC (Alphabet Inc.)
    • 6.4.5 Fair Isaac Corporation
    • 6.4.6 SAS Institute Inc.
    • 6.4.7 Intel Corporation
    • 6.4.8 NVIDIA Corporation
    • 6.4.9 Salesforce, Inc.
    • 6.4.10 Stripe, Inc.
    • 6.4.11 Plaid Inc.
    • 6.4.12 ComplyAdvantage Ltd.
    • 6.4.13 Onfido Ltd.
    • 6.4.14 Ripple Labs Inc.
    • 6.4.15 DataRobot, Inc.
    • 6.4.16 Upstart Holdings, Inc.
    • 6.4.17 ZestFinance, Inc.
    • 6.4.18 Darktrace Limited
    • 6.4.19 Truera, Inc.
    • 6.4.20 Active Intelligence Pte Ltd.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-need Assessment