세계의 핀테크용 에이전트형 AI 시장 예측(-2032년) : 기능별, 전개 방식별, 조직 규모별, 기술별, 용도별, 최종사용자별, 지역별 분석

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.