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市场调查报告书
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1865440

全球金融科技领域以代理为基础的人工智慧市场：预测（至2032年）－按功能、部署方式、组织规模、技术、应用、最终用户和地区进行分析

Agentic AI in Fintech Market Forecasts to 2032 - Global Analysis By Functionality, Deployment Mode, Organization Size, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，全球金融科技领域基于代理的人工智慧市场预计在 2025 年达到 99 亿美元，预计到 2032 年将达到 1,248 亿美元，在预测期内的复合年增长率为 43.6%。

面向金融科技领域的基于代理的人工智慧是指无需持续人工干预即可自主决策并主动进行金融行为的人工智慧系统。与仅分析资料的传统人工智慧不同，基于代理的人工智慧具有明确的意图。具体而言，它可以协商、优化并执行诸如诈欺预防、投资策略、信用风险评估和个人化银行服务等任务。这些人工智慧代理运用自适应推理，并不断从结果中学习以提升自身效能。在金融科技领域，这种自主性能够实现即时财务洞察、预测建模和增强客户参与。透过整合以代理为基础的人工智慧，金融机构可以获得更智慧、更自主的系统，进而提升数位金融营运的效率、准确性和策略创新能力。

财务营运自动化

金融营运自动化是推动金融科技领域基于代理的人工智慧市场发展的关键驱动力。基于代理的人工智慧系统能够以最少的人工干预简化诈欺侦测、信用评分和投资组合管理等复杂任务。这些自主代理能够提高速度、准确性和扩充性，从而降低营运成本并改善决策。金融机构可以从即时洞察和自适应学习中受益，实现更智慧的工作流程和个人化服务。随着数位金融的演进，基于代理的人工智慧自动化对于保持竞争优势和卓越营运至关重要。

高昂的实施成本

高昂的实施成本是金融科技领域广泛采用基于代理的人工智慧的一大障碍。开发、整合和维护先进的人工智慧系统需要对基础设施、专业人才和持续的模型训练进行大量投资。小型金融机构难以负担这些费用，减缓了市场民主化的进程。这些成本也阻碍了实验和创新，限制了扩充性，并减缓了人工智慧主导的金融生态系统转型步伐。

数位转型进展

持续的数位转型为金融科技领域的基于代理的人工智慧带来了巨大的机会。金融机构正在快速推动服务数位化，以满足不断变化的客户期望和监管要求。基于代理的人工智慧透过建立主动式智慧系统来促进这一转型，这些系统能够实现个人化体验、优化营运并预测市场趋势。与云端运算、物联网和区块链的整合进一步扩展了其功能。随着金融科技生态系统的发展，以代理为基础的人工智慧将成为创新的基石，推动全球市场实现更智慧、更快速、更安全的金融服务。

资料隐私和安全风险

资料隐私和安全风险对金融科技领域基于代理的人工智慧的发展构成重大障碍。处理敏感的金融资料会增加资料外洩、滥用和监管处罚的风险。对未授权存取和遵守严格的资料保护法律的担忧阻碍了人工智慧的普及。这些风险会削弱客户信任，减缓创新，并迫使企业在网路安全方面投入大量资金，进一步加剧营运预算压力，并延缓人工智慧的大规模应用。

新冠疫情的影响：

新冠疫情凸显了建构高弹性自动化系统的重要性，并加速了金融科技领域基于代理的人工智慧技术的应用。远距办公和数位银行的激增促使金融机构部署人工智慧代理，用于诈欺检测、客户支援和财务规划。这场危机凸显了即时洞察和自适应技术的价值。儘管疫情初期的干扰影响了部署进度，但疫情后的復苏正加速对自主人工智慧的投资。疫情重塑了金融科技的优先事项，并将基于代理的人工智慧确立为未来金融服务的关键工具。

预计在预测期内，演算法交易板块将占据最大的市场份额。

预计在预测期内，演算法交易领域将占据最大的市场份额。这是因为基于代理的人工智慧能够自主分析市场数据、执行交易并适应即时市场状况，从而增强交易策略。这些系统透过学习交易结果并优化自身效能，超越了传统模型。金融机构正在利用基于代理的人工智慧来降低延迟、管理风险并掌握市场机会。随着演算法交易日趋成熟，其在金融科技人工智慧应用领域的领先地位也将持续扩大。

预计银行业在预测期内将呈现最高的复合年增长率。

预计在预测期内，银行业将实现最高成长率，因为基于代理商的人工智慧正在透过自动化客户服务、个人化金融咨询和简化后勤部门营运来变革银行业务。这些智慧代理能够实现即时诈欺侦测、信用评估和交易监控，从而提高安全性和效率。银行正在投资人工智慧驱动的平台，以增强客户参与和营运灵活性。随着数位银行的扩张，基于代理的人工智慧已成为提供无缝和主动服务的关键，推动着该行业的快速成长和创新。

占比最大的地区：

预计亚太地区将在预测期内占据最大的市场份额，这主要得益于该地区蓬勃发展的金融科技生态系统、庞大的拥有者银行帐户以及政府对推动数位创新的支持，这些因素都在推动人工智慧的普及应用。中国、印度和新加坡等国家在金融服务领域的人工智慧整合方面处于领先地位。行动装置的快速普及和精通技术的消费者进一步推动了市场需求。金融机构正在采用基于代理的人工智慧来改善客户体验、预防诈欺并扩大服务范围。亚太地区充满活力的市场环境使其成为全球金融科技人工智慧领域的领导者。

预计年复合成长率最高的地区：

预计北美在预测期内将实现最高的复合年增长率，这得益于该地区先进的金融基础设施、良好的投资环境以及对人工智慧技术的早期应用。美国和加拿大的企业正在利用基于代理的人工智慧实现个人化银行服务、预测分析和自主交易。监管政策的明朗化和创新中心的建立正在加速这一发展。随着数位转型的深入，北美对智慧自动化和数据驱动型金融的重视使其在采用基于代理的人工智慧方面处于主导。

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  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
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目录

第一章执行摘要

第二章 引言

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

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

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

第五章 全球金融科技领域以代理为基础的人工智慧市场（按功能划分）

  • 介绍
  • 预测分析与规范分析
  • 自动化决策系统
  • 对话式和咨询式代理
  • 自主金融代理人

第六章 以部署方式分類的全球金融科技领域是基于代理的人工智慧市场

  • 介绍
  • 云端基础的
  • 本地部署
  • 杂交种

第七章 依组织规模分類的全球金融科技领域基于代理的人工智慧市场

  • 介绍
  • 大公司
  • 小型企业

8. 全球金融科技领域以代理为基础的人工智慧市场（按技术划分）

  • 介绍
  • 机器学习/深度学习
  • 自然语言处理（NLP）
  • 强化学习
  • 多智能体系统
  • 生成式人工智慧/法学硕士
  • 预测分析
  • RPA/认知自动化

第九章 全球金融科技领域以代理为基础的人工智慧市场（按应用划分）

  • 介绍
  • 诈骗侦测和风险管理
  • 客户服务自动化
  • 信用评分和承保
  • 演算法交易
  • 高净值人士，资产管理
  • 合规与监管自动化
  • 财务咨询和决策支援

第十章：全球金融科技领域以代理为基础的人工智慧市场（按最终用户划分）

  • 介绍
  • 银行业
  • 保险
  • 投资公司
  • 付款闸道
  • 信用报告机构
  • 金融科技Start-Ups
  • 监管机构

第十一章 全球金融科技领域以代理为基础的人工智慧市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章：主要趋势

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

第十三章：公司简介

  • OpenAI
  • Microsoft Corporation
  • Alphabet Inc.（Google）
  • Anthropic
  • NVIDIA Corporation
  • IBM Corporation
  • Amazon Web Services（AWS）
  • AppZen
  • Stripe, Inc.
  • Visa Inc.
  • Mastercard Incorporated
  • PayPal Holdings, Inc.
  • JPMorgan Chase & Co.
  • Wells Fargo & Company
  • UiPath, Inc.
Product Code: SMRC32127

According to Stratistics MRC, the Global Agentic AI in Fintech Market is accounted for $9.9 billion in 2025 and is expected to reach $124.8 billion by 2032 growing at a CAGR of 43.6% during the forecast period. Agentic AI in fintech refers to artificial intelligence systems capable of autonomous decision-making and proactive financial actions without constant human input. Unlike traditional AI, which merely analyzes data, agentic AI acts with intent-negotiating, optimizing, and executing tasks such as fraud prevention, investment strategies, credit risk assessment, and personalized banking services. These AI agents operate with adaptive reasoning, continuously learning from outcomes to improve performance. In fintech, this autonomy enables real-time financial insights, predictive modeling, and enhanced customer engagement. By integrating agentic AI, financial institutions gain smarter, self-directed systems that drive efficiency, accuracy, and strategic innovation across digital finance operations.

Market Dynamics:

Driver:

Automation of Financial Operations

Automation of financial operations is a key driver of the Agentic AI in Fintech Market. Agentic AI systems streamline complex tasks such as fraud detection, credit scoring, and portfolio management with minimal human intervention. These autonomous agents enhance speed, accuracy, and scalability, reducing operational costs and improving decision-making. Financial institutions benefit from real-time insights and adaptive learning, enabling smarter workflows and personalized services. As digital finance evolves, automation powered by agentic AI becomes essential for competitive advantage and operational excellence.

Restraint:

High Implementation Costs

High implementation costs significantly hinder the adoption of agentic AI in the fintech market. Developing, integrating, and maintaining advanced AI systems demand substantial investment in infrastructure, skilled personnel, and continuous model training. Smaller financial institutions struggle to justify such expenses, slowing market democratization. These costs also raise barriers to experimentation and innovation, limiting scalability and reducing the overall pace of AI-driven transformation across the financial ecosystem.

Opportunity:

Rising Digital Transformation

Rising digital transformation presents a major opportunity for the Agentic AI in Fintech Market. Financial institutions are rapidly digitizing services to meet evolving customer expectations and regulatory demands. Agentic AI enhances this shift by enabling proactive, intelligent systems that personalize experiences, optimize operations, and predict market trends. Integration with cloud computing, IoT, and blockchain further expands capabilities. As fintech ecosystems grow, agentic AI becomes a cornerstone of innovation, driving smarter, faster, and more secure financial services across global markets.

Threat:

Data Privacy & Security Risks

Data privacy and security risks pose a major hindrance to the growth of agentic AI in the fintech market. Handling sensitive financial data increases vulnerability to breaches, misuse, and regulatory penalties. Concerns over unauthorized access and compliance with stringent data protection laws discourage adoption. These risks erode customer trust, delay innovation, and compel firms to invest heavily in cybersecurity, further straining operational budgets and slowing large-scale AI deployment.

Covid-19 Impact:

The COVID-19 pandemic accelerated the adoption of Agentic AI in fintech by highlighting the need for resilient, automated systems. Remote operations and digital banking surged, prompting institutions to deploy AI agents for fraud detection, customer support, and financial planning. The crisis underscored the value of real-time insights and adaptive technologies. While initial disruptions affected implementation timelines, post-pandemic recovery has fueled investment in autonomous AI. The pandemic reshaped fintech priorities, positioning agentic AI as a vital tool for future-proofing financial services.

The algorithmic trading segment is expected to be the largest during the forecast period

The algorithmic trading segment is expected to account for the largest market share during the forecast period, as Agentic AI enhances trading strategies by autonomously analyzing market data, executing trades, and adapting to real-time conditions. These systems outperform traditional models by learning from outcomes and optimizing performance. Financial firms leverage agentic AI to reduce latency, manage risk, and capitalize on market opportunities. As algorithmic trading becomes more sophisticated, its dominance in fintech AI applications continues to grow.

The banking segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the banking segment is predicted to witness the highest growth rate, as Agentic AI transforms banking by automating customer service, personalizing financial advice, and streamlining back-office operations. These intelligent agents enable real-time fraud detection, credit assessments, and transaction monitoring, enhancing security and efficiency. Banks are investing in AI-driven platforms to improve customer engagement and operational agility. As digital banking expands, agentic AI becomes integral to delivering seamless, proactive services, driving rapid growth and innovation in the sector.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, due to region's booming fintech ecosystem, large unbanked population, and government support for digital innovation drive adoption. Countries like China, India, and Singapore are leading in AI integration across financial services. Rapid mobile penetration and tech-savvy consumers' further fuels demand. Financial institutions are deploying agentic AI to enhance customer experience, reduce fraud, and expand access. Asia Pacific's dynamic market conditions make it a global leader in fintech AI.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to region's advanced financial infrastructure, strong investment landscape, and early adoption of AI technologies support rapid growth. U.S. and Canadian firms are leveraging agentic AI for personalized banking, predictive analytics, and autonomous trading. Regulatory clarity and innovation hubs accelerate development. As digital transformation intensifies, North America's focus on intelligent automation and data-driven finance positions it for leadership in agentic AI adoption.

Key players in the market

Some of the key players in Agentic AI in Fintech Market include OpenAI, Microsoft Corporation, Alphabet Inc. (Google), Anthropic, NVIDIA Corporation, IBM Corporation, Amazon Web Services (AWS), AppZen, Stripe, Inc., Visa Inc., Mastercard Incorporated, PayPal Holdings, Inc., JPMorgan Chase & Co., Wells Fargo & Company, and UiPath, Inc.

Key Developments:

In October 2025, Microsoft Corporation has deepened its partnership with OpenAI through a definitive agreement that values its investment at approximately US$135 billion, giving Microsoft a 27 % ownership stake in the newly recapitalised OpenAI Group PBC and extending its intellectual-property rights through 2032 while validating any artificial general intelligence (AGI) via an independent expert panel.

In March 2025, Microsoft Corporation has strengthened its strategic partnership with the Government of Kuwait, planning to launch an AI-powered Azure region to accelerate national digital transformation, drive economic growth, foster AI innovation and prepare the workforce for the future.

Functionalities Covered:

  • Predictive and Prescriptive Analytics
  • Automated Decision-making Systems
  • Conversational and Advisory Agents
  • Autonomous Financial Agents

Deployment Modes Covered:

  • Cloud-based
  • On-premises
  • Hybrid

Organization Sizes Covered:

  • Large Enterprises
  • Small and Medium Enterprises (SMEs)

Technologies Covered:

  • Machine Learning and Deep Learning
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Multi-Agent Systems
  • Generative AI and LLMs
  • Predictive Analytics
  • RPA and Cognitive Automation

Applications Covered:

  • Fraud Detection and Risk Management
  • Customer Service Automation
  • Credit Scoring and Underwriting
  • Algorithmic Trading
  • Wealth and Asset Management
  • Compliance and Regulatory Automation
  • Financial Advisory and Decision Support

End Users Covered:

  • Banking
  • Insurance
  • Investment Firms
  • Payment Gateways
  • Credit Bureaus
  • Fintech Startups
  • Regulatory Bodies

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 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 Agentic AI in Fintech Market, By Functionality

  • 5.1 Introduction
  • 5.2 Predictive and Prescriptive Analytics
  • 5.3 Automated Decision-making Systems
  • 5.4 Conversational and Advisory Agents
  • 5.5 Autonomous Financial Agents

6 Global Agentic AI in Fintech Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-based
  • 6.3 On-premises
  • 6.4 Hybrid

7 Global Agentic AI in Fintech Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Large Enterprises
  • 7.3 Small and Medium Enterprises (SMEs)

8 Global Agentic AI in Fintech Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning and Deep Learning
  • 8.3 Natural Language Processing (NLP)
  • 8.4 Reinforcement Learning
  • 8.5 Multi-Agent Systems
  • 8.6 Generative AI and LLMs
  • 8.7 Predictive Analytics
  • 8.8 RPA and Cognitive Automation

9 Global Agentic AI in Fintech Market, By Application

  • 9.1 Introduction
  • 9.2 Fraud Detection and Risk Management
  • 9.3 Customer Service Automation
  • 9.4 Credit Scoring and Underwriting
  • 9.5 Algorithmic Trading
  • 9.6 Wealth and Asset Management
  • 9.7 Compliance and Regulatory Automation
  • 9.8 Financial Advisory and Decision Support

10 Global Agentic AI in Fintech Market, By End User

  • 10.1 Introduction
  • 10.2 Banking
  • 10.3 Insurance
  • 10.4 Investment Firms
  • 10.5 Payment Gateways
  • 10.6 Credit Bureaus
  • 10.7 Fintech Startups
  • 10.8 Regulatory Bodies

11 Global Agentic AI in Fintech Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 OpenAI
  • 13.2 Microsoft Corporation
  • 13.3 Alphabet Inc. (Google)
  • 13.4 Anthropic
  • 13.5 NVIDIA Corporation
  • 13.6 IBM Corporation
  • 13.7 Amazon Web Services (AWS)
  • 13.8 AppZen
  • 13.9 Stripe, Inc.
  • 13.10 Visa Inc.
  • 13.11 Mastercard Incorporated
  • 13.12 PayPal Holdings, Inc.
  • 13.13 JPMorgan Chase & Co.
  • 13.14 Wells Fargo & Company
  • 13.15 UiPath, Inc.

List of Tables

  • Table 1 Global Agentic AI in Fintech Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Agentic AI in Fintech Market Outlook, By Functionality (2024-2032) ($MN)
  • Table 3 Global Agentic AI in Fintech Market Outlook, By Predictive and Prescriptive Analytics (2024-2032) ($MN)
  • Table 4 Global Agentic AI in Fintech Market Outlook, By Automated Decision-making Systems (2024-2032) ($MN)
  • Table 5 Global Agentic AI in Fintech Market Outlook, By Conversational and Advisory Agents (2024-2032) ($MN)
  • Table 6 Global Agentic AI in Fintech Market Outlook, By Autonomous Financial Agents (2024-2032) ($MN)
  • Table 7 Global Agentic AI in Fintech Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 8 Global Agentic AI in Fintech Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 9 Global Agentic AI in Fintech Market Outlook, By On-premises (2024-2032) ($MN)
  • Table 10 Global Agentic AI in Fintech Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 11 Global Agentic AI in Fintech Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 12 Global Agentic AI in Fintech Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 13 Global Agentic AI in Fintech Market Outlook, By Small and Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 14 Global Agentic AI in Fintech Market Outlook, By Technology (2024-2032) ($MN)
  • Table 15 Global Agentic AI in Fintech Market Outlook, By Machine Learning and Deep Learning (2024-2032) ($MN)
  • Table 16 Global Agentic AI in Fintech Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 17 Global Agentic AI in Fintech Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 18 Global Agentic AI in Fintech Market Outlook, By Multi-Agent Systems (2024-2032) ($MN)
  • Table 19 Global Agentic AI in Fintech Market Outlook, By Generative AI and LLMs (2024-2032) ($MN)
  • Table 20 Global Agentic AI in Fintech Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 21 Global Agentic AI in Fintech Market Outlook, By RPA and Cognitive Automation (2024-2032) ($MN)
  • Table 22 Global Agentic AI in Fintech Market Outlook, By Application (2024-2032) ($MN)
  • Table 23 Global Agentic AI in Fintech Market Outlook, By Fraud Detection and Risk Management (2024-2032) ($MN)
  • Table 24 Global Agentic AI in Fintech Market Outlook, By Customer Service Automation (2024-2032) ($MN)
  • Table 25 Global Agentic AI in Fintech Market Outlook, By Credit Scoring and Underwriting (2024-2032) ($MN)
  • Table 26 Global Agentic AI in Fintech Market Outlook, By Algorithmic Trading (2024-2032) ($MN)
  • Table 27 Global Agentic AI in Fintech Market Outlook, By Wealth and Asset Management (2024-2032) ($MN)
  • Table 28 Global Agentic AI in Fintech Market Outlook, By Compliance and Regulatory Automation (2024-2032) ($MN)
  • Table 29 Global Agentic AI in Fintech Market Outlook, By Financial Advisory and Decision Support (2024-2032) ($MN)
  • Table 30 Global Agentic AI in Fintech Market Outlook, By End User (2024-2032) ($MN)
  • Table 31 Global Agentic AI in Fintech Market Outlook, By Banking (2024-2032) ($MN)
  • Table 32 Global Agentic AI in Fintech Market Outlook, By Insurance (2024-2032) ($MN)
  • Table 33 Global Agentic AI in Fintech Market Outlook, By Investment Firms (2024-2032) ($MN)
  • Table 34 Global Agentic AI in Fintech Market Outlook, By Payment Gateways (2024-2032) ($MN)
  • Table 35 Global Agentic AI in Fintech Market Outlook, By Credit Bureaus (2024-2032) ($MN)
  • Table 36 Global Agentic AI in Fintech Market Outlook, By Fintech Startups (2024-2032) ($MN)
  • Table 37 Global Agentic AI in Fintech Market Outlook, By Regulatory Bodies (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.