全球业务人工智慧市场规模、产品、应用、地区及预测

人工智慧在业务市场规模及预测

预计2024年业务人工智慧市场规模将达到116.2亿美元，到2032年将达到909.7亿美元，在2026-2032年预测期间的复合年增长率为32.36%。

业务人工智慧是指将人工智慧技术融入银行各项业务，以提高业务效率、客户体验和决策能力。人工智慧 (AI) 在业务的应用包括高阶数据分析、自然语言处理 (NLP)、机器学习 (ML)、机器人流程自动化 (RPA) 等。

最重要的应用之一是诈欺侦测和预防，人工智慧系统分析大量交易资料以发现可疑趋势并即时提醒银行潜在风险，帮助他们减少财务损失并保护客户免受诈骗。

随着科技的进步，人工智慧在业务的应用预计将不断扩展，并更加自动化和客製化。人工智慧的数据分析能力将使银行能够根据每位客户的需求和偏好，提供高度个人化的金融产品和服务。

全球业务人工智慧市场动态

影响全球银行业人工智慧业务的关键市场动态是：

关键市场驱动因素

诈欺侦测和风险管理需求日益增长：随着金融犯罪日益复杂和频繁，银行正转向人工智慧解决方案来即时侦测诈欺活动。人工智慧能够分析大量交易资料、发现模式并标记异常，使其成为降低风险的重要工具。

透过个人化提升客户体验：人工智慧 (AI) 在提升银行业客户服务方面发挥关键作用。借助人工智慧聊天机器人、虚拟助理和个人化提案，银行可以为消费者提供量身定制的解决方案。银行可以利用人工智慧监控消费者的行为、偏好和交易历史，从而客製化满足个人需求的金融产品和服务。

提高业务效率并降低成本：人工智慧技术透过自动化贷款申请处理、文件验证和客户服务等常规和重复流程来帮助银行。自动化减少了人机互动的需求，加快了流程速度并降低了出错的可能性。透过简化流程，人工智慧降低了营运成本，使银行能够更有效地分配资源，专注于更高价值的业务。

主要问题

资料隐私与安全性随着银行越来越多地机会人工智慧分析大量客户数据，保护这些敏感资讯的隐私和安全至关重要。遵守欧洲《一般资料保护规则》(GDPR) 和美国《加州消费者隐私法案》(CCPA) 等法规是一项重大挑战。

与旧有系统整合：许多银行使用的旧有系统与现代人工智慧技术不相容。将人工智慧解决方案整合到过时的基础设施中可能非常复杂、成本高昂业务。

人才短缺：人工智慧技术的快速发展需要资料科学、机器学习和人工智慧应用的人才。然而，这些领域存在严重的人才短缺，导致银行难以找到并留住优秀员工。这种人才缺口可能会阻碍人工智慧的成功应用和运营，从而限制银行有效利用人工智慧的能力。

主要趋势

提升客户体验：银行越来越多地利用人工智慧来提供个人化的客户体验。人工智慧聊天机器人和虚拟助理正被用于提供全天候客户服务，轻鬆处理客户咨询和交易。透过评估客户数据，银行可以个人化提案和服务，从而提高客户满意度和忠诚度。

诈欺侦测与预防：随着网路风险的不断演变，人工智慧技术在改善银行安全程序方面发挥着日益重要的作用。机器学习演算法可以即时评估交易模式，并侦测可能存在诈欺行为的异常行为。透过自动化诈欺侦测，银行可以更快应对潜在威胁，限制财务损失，并维护客户信心。

风险管理与合规：人工智慧正在透过更精准的风险评估，改变银行的风险管理业务。银行可以利用先进的分析和预测模型来识别贷款、投资和监管合规的潜在风险。

目录

第一章 全球业务人工智慧市场应用情况

• 市场概览
• 研究范围
• 先决条件

第二章执行摘要

第三章：已验证的市场研究调查方法

• 资料探勘
• 验证
• 第一手资料
• 资料来源列表

第四章 全球业务人工智慧市场展望

• 概述
• 市场动态
  • 驱动程式
  • 限制因素
  • 机会
• 波特五力模型
• 价值链分析

第五章全球银行业人工智慧市场（按产品）

• 概述
• 硬体
• 软体
• 服务

第六章 全球业务人工智慧应用市场

• 概述
• 分析
• 聊天机器人
• 机器人流程自动化

7. 全球业务人工智慧市场（按地区）

• 概述
• 北美洲
  • 美国
  • 加拿大
  • 墨西哥
• 欧洲
  • 德国
  • 英国
  • 法国
  • 义大利
  • 西班牙
  • 其他欧洲国家
• 亚太地区
  • 中国
  • 日本
  • 印度
  • 其他亚太地区
• 其他的
  • 拉丁美洲
  • 中东和非洲

8. 全球业务人工智慧市场竞争格局

• 概述
• 各公司市场排名
• 重点发展策略

第九章 公司简介

• Intel
• Harman International Industries
• Cisco Systems
• ABB
• IBM Corp
• Nuance Corporation
• Google LLC
• Accenture
• IPsoft, Inc.
• Bsh Hausgerate
• Hanson Robotics
• Blue Frog Robotics
• Fanuc

第十章 重大进展

• 产品发布/开发
• 合併与收购
• 业务扩展
• 伙伴关係与合作

第十一章 附录

• 相关调查

AI in Banking Market Size and Forecast

AI in Banking Market size was valued at USD 11.62 Billion in 2024 and is projected to reach USD 90.97 Billion by 2032, growing at a CAGR of 32.36% from 2026 to 2032.

AI in banking is the integration of artificial intelligence technologies into various banking operations to improve operational efficiency, client experience, and decision-making abilities. Artificial intelligence (AI) applications in banking include sophisticated data analytics, natural language processing (NLP), machine learning (ML), and robotic process automation (RPA).

One of the most important applications is fraud detection and prevention in which AI systems analyze massive volumes of transactional data to discover suspicious trends and alert potential risks in real time. This enables banks to reduce financial losses and safeguard clients from fraud.

The future application of AI in banking is projected to grow as technology advances, resulting in even greater automation and customisation. AI's data analytics capabilities will allow banks to offer highly personalized financial products and services based on individual client demands and preferences.

Global AI in Banking Market Dynamics

The key market dynamics that are shaping global AI in the banking market include:

Key Market Drivers:

Increasing Demand for Fraud Detection and Risk Management: As financial crimes become more complicated and frequent, banks are turning to AI-powered solutions to detect fraudulent activity in real-time. AI's ability to analyze massive volumes of transactional data, find patterns, and flag anomalies has made it an essential tool for risk mitigation.

Improving Customer Experience with Personalization: Artificial intelligence (AI) plays an important role in improving customer service in the banking sector. Banks may provide bespoke solutions to their consumers by using AI-powered chatbots, virtual assistants, and personalized suggestions. Banks can use AI to monitor consumer behavior, preferences, and transaction histories, allowing them to tailor financial goods and services to individual needs.

Operational Efficiency and Cost Reduction: AI technologies assist banks in automating routine and repetitive processes such as loan application processing, document verification, and customer service. Automation decreases the need for human interaction, speeds up procedures, and lowers the chance of error. By streamlining procedures, AI decreases operating costs allowing banks to allocate resources more efficiently and focus on higher-value activities.

Key Challenges:

Data Privacy and Security: As banks increasingly use AI to analyze massive volumes of client data, protecting the privacy and security of this sensitive information becomes critical. Regulatory compliance such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States presents substantial hurdles.

Integration with Legacy Systems: Many banks still use legacy systems which may not be compatible with modern AI technologies. Integrating AI solutions with antiquated infrastructure can be complicated and costly, potentially disrupting operations.

Talent Scarcity: The rapid expansion of AI technology needs a workforce proficient in data science, machine learning, and AI applications. However, there is a considerable talent shortage in these disciplines making it difficult for banks to find and keep talented employees. This talent gap can inhibit the successful adoption and administration of AI efforts limiting the bank's capacity to employ AI successfully.

Key Trends:

Enhanced Customer Experience: Banks are increasingly using AI to provide individualized customer experiences. AI-powered chatbots and virtual assistants are being utilized to provide 24-hour customer service handling inquiries and transactions with ease. By evaluating client data, banks can personalize product suggestions and services, increasing customer happiness and loyalty.

Fraud Detection and Prevention: As cyber risks evolve, AI technologies play an increasingly important role in improving bank security procedures. Machine learning algorithms evaluate transaction patterns in real-time to detect odd behavior that could signal fraud. Banks can respond faster to potential threats by automating fraud detection, lowering financial losses, and maintaining customer trust.

Risk Management and Compliance: Artificial intelligence is altering bank's risk management operations by allowing for more accurate risk assessments. Banks can use advanced analytics and predictive modeling to identify possible hazards in lending, investments, and regulatory compliance.

Global AI in Banking Market Regional Analysis

Here is a more detailed regional analysis of the global AI in the banking market:

North America:

North America dominates the worldwide AI banking industry owing to its superior technological infrastructure and early adoption of AI solutions by key financial institutions. This supremacy is mostly fueled by the United States, which accounts for the majority of AI investments in the banking industry. The need for improved customer experience and personalization has been a major driver of AI adoption in North American banking.

According to Federal Reserve research, 76% of Americans would use mobile banking apps in 2024, up from 65% in 2020, creating a favorable environment for AI-powered personalized services. According to the American Bankers Association (ABA), 71% of banks are now employing or planning to use artificial intelligence to improve customer service.

According to a Thomson Reuters analysis, regulatory compliance costs US financial companies USD 270 Billion each year. AI is viewed as a critical tool in cost management, with 63% of banks planning to boost their AI investments in regulatory technology by 2025, according to the Financial Stability Board. Gartner predicts that North American banks will invest USD 37.5 Billion in AI technologies by 2025, expanding at a 22.6% CAGR. Government programs promote this expansion, such as the U.S.

Asia Pacific:

The Asia Pacific region is experiencing fastest growth in AI usage in the banking sector owing to rapid digital transformation and increased fintech investments. This rapid expansion is being driven by the region's enormous population, increased internet penetration, and government measures promoting technological breakthroughs in financial services.

The increased desire for tailored financial services and better client experiences is a major driver of AI in banking in the Asia Pacific. According to the Asian Development Bank's (ADB) report, 78% of regional banks intend to deploy AI-driven customization by 2025.

The need for operational efficiency is also driving AI adoption in banking. According to McKinsey & Company, AI technologies have the potential to add up to $1 trillion in value to the global banking industry each year, with Asia-Pacific institutions positioned to benefit significantly. The region's fintech investments have been significant, with KPMG projecting that fintech funding in Asia Pacific will reach USD 50.5 Billion in 2024, up 44% from the previous year. Government assistance has been critical, with efforts such as Singapore's AI Governance Framework and China's New Generation Artificial Intelligence Development Plan promoting AI development.

Global AI in Banking Market: Segmentation Analysis

The Global AI in Banking Market is segmented based on Product, Application, Technology, and Geography.

AI in Banking Market, By Product

• Hardware
• Software
• Services

Based on the Product, the Global AI in Banking Market is bifurcated into Hardware, Software, and Services. The software segment is dominant in the AI banking market driven by the widespread adoption of AI-powered solutions such as fraud detection, risk management, and customer service chatbots. Banks are increasingly relying on advanced software applications to automate complex processes, analyze large datasets, and enhance decision-making accuracy. AI software enables financial institutions to improve operational efficiency, personalize customer experiences, and detect anomalies in real time, which are critical in a competitive banking landscape.

AI in Banking Market, By Application

• Analytics
• Chatbots
• Robotic Process Automation (RPA)

Based on the Application, the Global AI in Banking Market is bifurcated into Analytics, Chatbots, and Robotic Process Automation (RPA). Among the applications of AI in banking, analytics is the dominant segment due to its critical role in enhancing decision-making, risk management, and personalized customer experiences. Banks increasingly rely on AI-driven analytics to process vast amounts of data, identifying patterns, trends, and anomalies that help optimize operations, detect fraud, and assess credit risk more accurately. This data-driven approach enables banks to improve customer targeting, reduce operational costs, and enhance overall efficiency. Additionally, predictive analytics allows for proactive financial planning and portfolio management.

AI in Banking Market, By Geography

• North America
• Europe
• Asia Pacific
• Rest of the World

Based on Geography, the Global AI in Banking Market is classified into North America, Europe, Asia Pacific, and the Rest of the World. North America is the dominant region in the AI banking market driven by the rapid adoption of advanced technologies and a highly developed banking infrastructure. Major financial institutions in the U.S. and Canada are leveraging AI for various applications such as fraud detection, personalized banking services, risk management, and customer service automation through AI-powered chatbots. The region's strong emphasis on innovation coupled with significant investments in AI research and development has accelerated the integration of AI in banking operations.

Key Players

The "Global AI in Banking Market" study report will provide valuable insight with an emphasis on the global market. The major players in the market are Intel, Harman International Industries, Cisco Systems, ABB, IBM Corp, Nuance Corporation, Google LLC, Accenture, IPsoft, Inc., Bsh Hausgerate, Hanson Robotics, Blue Frog Robotics, and Fanuc.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also included as key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Global AI in Banking Market Key Developments

• In November 2024, Amazon Web Services, Inc. announced that the Bank of Ayudhya Public Company Limited (Krungsri) in Thailand will use AWS to boost customer experiences and financial inclusion efforts.
• In May 2024, Temenos, a Swiss software company, announced a partnership with Amazon Web Services, Inc. (AWS) to deliver core banking solutions via a Software-as-a-Service (SaaS) paradigm, seamlessly integrating its application with AWS infrastructure.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL AI IN BANKING MARKET

• 1.1 Overview of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL AI IN BANKING MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL AI IN BANKING MARKET, BY PRODUCT

• 5.1 Overview
• 5.2 Hardware
• 5.3 Software
• 5.4 Services

6 GLOBAL AI IN BANKING MARKET, BY APPLICATION

• 6.1 Overview
• 6.2 Analytics
• 6.3 Chatbots
• 6.4 Robotic Process Automation

7 GLOBAL AI IN BANKING MARKET, BY GEOGRAPHY

• 7.1 Overview
• 7.2 North America
  • 7.2.1 U.S.
  • 7.2.2 Canada
  • 7.2.3 Mexico
• 7.3 Europe
  • 7.3.1 Germany
  • 7.3.2 U.K.
  • 7.3.3 France
  • 7.3.4 Italy
  • 7.3.5 Spain
  • 7.3.6 Rest of Europe
• 7.4 Asia Pacific
  • 7.4.1 China
  • 7.4.2 Japan
  • 7.4.3 India
  • 7.4.4 Rest of Asia Pacific
• 7.5 Rest of the World
  • 7.5.1 Latin America
  • 7.5.2 Middle East and Africa

8 GLOBAL AI IN BANKING MARKET COMPETITIVE LANDSCAPE

• 8.1 Overview
• 8.2 Company Market Ranking
• 8.3 Key Development Strategies

9 COMPANY PROFILES

• 9.1 Intel
  • 9.1.1 Company Overview
  • 9.1.2 Company Insights
  • 9.1.3 Business Breakdown
  • 9.1.4 Product Benchmarking
  • 9.1.5 Key Developments
  • 9.1.6 Winning Imperatives
  • 9.1.7 Current Focus & Strategies
  • 9.1.8 Threat from Competition
  • 9.1.9 SWOT Analysis
• 9.2 Harman International Industries
  • 9.2.1 Company Overview
  • 9.2.2 Company Insights
  • 9.2.3 Business Breakdown
  • 9.2.4 Product Benchmarking
  • 9.2.5 Key Developments
  • 9.2.6 Winning Imperatives
  • 9.2.7 Current Focus & Strategies
  • 9.2.8 Threat from Competition
  • 9.2.9 SWOT Analysis
• 9.3 Cisco Systems
  • 9.3.1 Company Overview
  • 9.3.2 Company Insights
  • 9.3.3 Business Breakdown
  • 9.3.4 Product Benchmarking
  • 9.3.5 Key Developments
  • 9.3.6 Winning Imperatives
  • 9.3.7 Current Focus & Strategies
  • 9.3.8 Threat from Competition
  • 9.3.9 SWOT Analysis
• 9.4 ABB
  • 9.4.1 Company Overview
  • 9.4.2 Company Insights
  • 9.4.3 Business Breakdown
  • 9.4.4 Product Benchmarking
  • 9.4.5 Key Developments
  • 9.4.6 Winning Imperatives
  • 9.4.7 Current Focus & Strategies
  • 9.4.8 Threat from Competition
  • 9.4.9 SWOT Analysis
• 9.5 IBM Corp
  • 9.5.1 Company Overview
  • 9.5.2 Company Insights
  • 9.5.3 Business Breakdown
  • 9.5.4 Product Benchmarking
  • 9.5.5 Key Developments
  • 9.5.6 Winning Imperatives
  • 9.5.7 Current Focus & Strategies
  • 9.5.8 Threat from Competition
  • 9.5.9 SWOT Analysis
• 9.6 Nuance Corporation
  • 9.6.1 Company Overview
  • 9.6.2 Company Insights
  • 9.6.3 Business Breakdown
  • 9.6.4 Product Benchmarking
  • 9.6.5 Key Developments
  • 9.6.6 Winning Imperatives
  • 9.6.7 Current Focus & Strategies
  • 9.6.8 Threat from Competition
  • 9.6.9 SWOT Analysis
• 9.7 Google LLC
  • 9.7.1 Company Overview
  • 9.7.2 Company Insights
  • 9.7.3 Business Breakdown
  • 9.7.4 Product Benchmarking
  • 9.7.5 Key Developments
  • 9.7.6 Winning Imperatives
  • 9.7.7 Current Focus & Strategies
  • 9.7.8 Threat from Competition
  • 9.7.9 SWOT Analysis
• 9.8 Accenture
  • 9.8.1 Company Overview
  • 9.8.2 Company Insights
  • 9.8.3 Business Breakdown
  • 9.8.4 Product Benchmarking
  • 9.8.5 Key Developments
  • 9.8.6 Winning Imperatives
  • 9.8.7 Current Focus & Strategies
  • 9.8.8 Threat from Competition
  • 9.8.9 SWOT Analysis
• 9.9 IPsoft, Inc.
  • 9.9.1 Company Overview
  • 9.9.2 Company Insights
  • 9.9.3 Business Breakdown
  • 9.9.4 Product Benchmarking
  • 9.9.5 Key Developments
  • 9.9.6 Winning Imperatives
  • 9.9.7 Current Focus & Strategies
  • 9.9.8 Threat from Competition
  • 9.9.9 SWOT Analysis
• 9.10 Bsh Hausgerate
  • 9.10.1 Company Overview
  • 9.10.2 Company Insights
  • 9.10.3 Business Breakdown
  • 9.10.4 Product Benchmarking
  • 9.10.5 Key Developments
  • 9.10.6 Winning Imperatives
  • 9.10.7 Current Focus & Strategies
  • 9.10.8 Threat from Competition
  • 9.10.9 SWOT Analysis
• 9.11 Hanson Robotics
  • 9.11.1 Company Overview
  • 9.11.2 Company Insights
  • 9.11.3 Business Breakdown
  • 9.11.4 Product Benchmarking
  • 9.11.5 Key Developments
  • 9.11.6 Winning Imperatives
  • 9.11.7 Current Focus & Strategies
  • 9.11.8 Threat from Competition
  • 9.11.9 SWOT Analysis
• 9.12 Blue Frog Robotics
  • 9.12.1 Company Overview
  • 9.12.2 Company Insights
  • 9.12.3 Business Breakdown
  • 9.12.4 Product Benchmarking
  • 9.12.5 Key Developments
  • 9.12.6 Winning Imperatives
  • 9.12.7 Current Focus & Strategies
  • 9.12.8 Threat from Competition
  • 9.12.9 SWOT Analysis
• 9.13 Fanuc
  • 9.13.1 Company Overview
  • 9.13.2 Company Insights
  • 9.13.3 Business Breakdown
  • 9.13.4 Product Benchmarking
  • 9.13.5 Key Developments
  • 9.13.6 Winning Imperatives
  • 9.13.7 Current Focus & Strategies
  • 9.13.8 Threat from Competition
  • 9.13.9 SWOT Analysis

10 KEY DEVELOPMENTS

• 10.1 Product Launches/Developments
• 10.2 Mergers and Acquisitions
• 10.3 Business Expansions
• 10.4 Partnerships and Collaborations

11 Appendix

• 11.1 Related Research