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全球金融科技領域以代理為基礎的人工智慧市場：預測（至2032年）－按功能、部署方式、組織規模、技術、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的一項研究，全球金融科技領域基於代理的人工智慧市場預計在 2025 年達到 99 億美元，預計到 2032 年將達到 1,248 億美元，在預測期內的複合年成長率為 43.6%。

面向金融科技領域的基於代理的人工智慧是指無需持續人工干預即可自主決策並主動進行金融行為的人工智慧系統。與僅分析資料的傳統人工智慧不同，基於代理的人工智慧具有明確的意圖。具體而言，它可以協商、最佳化並執行諸如詐欺預防、投資策略、信用風險評估和個人化銀行服務等任務。這些人工智慧代理運用自適應推理，並不斷從結果中學習以提升自身效能。在金融科技領域，這種自主性能夠實現即時財務洞察、預測建模和增強客戶參與。透過整合以代理為基礎的人工智慧，金融機構可以獲得更智慧、更自主的系統，進而提升數位金融營運的效率、準確性和策略創新能力。

財務營運自動化

金融營運自動化是推動金融科技領域基於代理的人工智慧市場發展的關鍵驅動力。基於代理的人工智慧系統能夠以最少的人工干預簡化詐欺偵測、信用評分和投資組合管理等複雜任務。這些自主代理能夠提高速度、準確性和擴充性，從而降低營運成本並改善決策。金融機構可以從即時洞察和自適應學習中受益，實現更智慧的工作流程和個人化服務。隨著數位金融的演進，基於代理的人工智慧自動化對於保持競爭優勢和卓越營運至關重要。

高昂的實施成本

高昂的實施成本是金融科技領域廣泛採用基於代理的人工智慧的一大障礙。開發、整合和維護先進的人工智慧系統需要對基礎設施、專業人才和持續的模型訓練進行大量投資。小型金融機構難以負擔這些費用，減緩了市場民主化的進程。這些成本也阻礙了實驗和創新，限制了擴充性，並減緩了人工智慧主導的金融生態系統轉型步伐。

數位轉型進展

持續的數位轉型為金融科技領域的基於代理的人工智慧帶來了巨大的機會。金融機構正在快速推動服務數位化，以滿足不斷變化的客戶期望和監管要求。基於代理的人工智慧透過建立主動式智慧系統來促進這一轉型，這些系統能夠實現個人化體驗、最佳化營運並預測市場趨勢。與雲端運算、物聯網和區塊鏈的整合進一步擴展了其功能。隨著金融科技生態系統的發展，以代理為基礎的人工智慧將成為創新的基石，推動全球市場實現更智慧、更快速、更安全的金融服務。

資料隱私和安全風險

資料隱私和安全風險對金融科技領域基於代理的人工智慧的發展構成重大障礙。處理敏感的金融資料會增加資料外洩、濫用和監管處罰的風險。對未授權存取和遵守嚴格的資料保護法律的擔憂阻礙了人工智慧的普及。這些風險會削弱客戶信任，減緩創新，並迫使企業在網路安全方面投入大量資金，進一步加劇營運預算壓力，並延緩人工智慧的大規模應用。

新冠疫情的影響：

新冠疫情凸顯了建構高彈性自動化系統的重要性，並加速了金融科技領域基於代理的人工智慧技術的應用。遠距辦公和數位銀行的激增促使金融機構部署人工智慧代理，用於詐欺檢測、客戶支援和財務規劃。這場危機凸顯了即時洞察和自適應技術的價值。儘管疫情初期的干擾影響了部署進度，但疫情後的復甦正加速對自主人工智慧的投資。疫情重塑了金融科技的優先事項，並將基於代理的人工智慧確立為未來金融服務的關鍵工具。

預計在預測期內，演算法交易板塊將佔據最大的市場佔有率。

預計在預測期內，演算法交易領域將佔據最大的市場佔有率。這是因為基於代理的人工智慧能夠自主分析市場數據、執行交易並適應即時市場狀況，從而增強交易策略。這些系統透過學習交易結果並最佳化自身效能，超越了傳統模型。金融機構正在利用基於代理的人工智慧來降低延遲、管理風險並掌握市場機會。隨著演算法交易日趨成熟，其在金融科技人工智慧應用領域的領先地位也將持續擴大。

預計銀行業在預測期內將呈現最高的複合年成長率。

預計在預測期內，銀行業將實現最高成長率，因為基於代理商的人工智慧正在透過自動化客戶服務、個人化金融諮詢和簡化後勤部門營運來變革銀行業務。這些智慧代理能夠實現即時詐欺偵測、信用評估和交易監控，從而提高安全性和效率。銀行正在投資人工智慧驅動的平台，以增強客戶參與和營運靈活性。隨著數位銀行的擴張，基於代理的人工智慧已成為提供無縫和主動服務的關鍵，推動著該行業的快速成長和創新。

佔比最大的地區：

預計亞太地區將在預測期內佔據最大的市場佔有率，這主要得益於該地區蓬勃發展的金融科技生態系統、龐大的擁有者銀行帳戶以及政府對推動數位創新的支持，這些因素都在推動人工智慧的普及應用。中國、印度和新加坡等國家在金融服務領域的人工智慧整合方面處於領先地位。行動裝置的快速普及和精通技術的消費者進一步推動了市場需求。金融機構正在採用基於代理的人工智慧來改善客戶體驗、預防詐欺並擴大服務範圍。亞太地區充滿活力的市場環境使其成為全球金融科技人工智慧領域的領導者。

預計年複合成長率最高的地區：

預計北美在預測期內將實現最高的複合年成長率，這得益於該地區先進的金融基礎設施、良好的投資環境以及對人工智慧技術的早期應用。美國和加拿大的企業正在利用基於代理的人工智慧實現個人化銀行服務、預測分析和自主交易。監管政策的明朗化和創新中心的建立正在加速這一發展。隨著數位轉型的深入，北美對智慧自動化和數據驅動型金融的重視使其在採用基於代理的人工智慧方面處於主導。

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目錄

第1章執行摘要

第2章 引言

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

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

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

第5章 全球金融科技領域以代理為基礎的人工智慧市場（按功能分類）

  • 介紹
  • 預測分析與規範分析
  • 自動化決策系統
  • 對話式和諮詢式代理
  • 自主金融代理人

第6章 以部署方式分類的全球金融科技領域是基於代理的人工智慧市場

  • 介紹
  • 雲端基礎的
  • 本地部署
  • 混合

第7章 依組織規模分類的全球金融科技領域基於代理的人工智慧市場

  • 介紹
  • 主要企業
  • 小型企業

8. 全球金融科技領域以代理為基礎的人工智慧市場（按技術分類）

  • 介紹
  • 機器學習/深度學習
  • 自然語言處理（NLP）
  • 強化學習
  • 多智慧體系統
  • 生成式人工智慧/法學碩士
  • 預測分析
  • RPA/認知自動化

第9章 全球金融科技領域以代理為基礎的人工智慧市場（按應用分類）

  • 介紹
  • 詐騙偵測和風險管理
  • 客戶服務自動化
  • 信用評分和承保
  • 演算法交易
  • 高淨值人士，資產管理
  • 合規與監管自動化
  • 財務諮詢和決策支援

第10章：全球金融科技領域以代理為基礎的人工智慧市場（按最終用戶分類）

  • 介紹
  • 銀行業
  • 保險
  • 投資公司
  • 付款閘道
  • 信用報告機構
  • 金融科技Start-Ups
  • 監管機構

第11章 全球金融科技領域以代理為基礎的人工智慧市場（按地區分類）

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章：主要趨勢

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

第13章：公司簡介

  • 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.