全球業務人工智慧市場規模、產品、應用、地區及預測

人工智慧在業務市場規模及預測

預計2024年業務人工智慧市場規模將達到116.2億美元，到2032年將達到909.7億美元，在2026-2032年預測期間的複合年成長率為32.36%。

業務人工智慧是指將人工智慧技術融入銀行各項業務，以提高業務效率、客戶體驗和決策能力。人工智慧 (AI) 在業務的應用包括高階數據分析、自然語言處理 (NLP)、機器學習 (ML)、機器人流程自動化 (RPA) 等。

最重要的應用之一是詐欺偵測和預防，人工智慧系統分析大量交易資料以發現可疑趨勢並即時提醒銀行潛在風險，幫助他們減少財務損失並保護客戶免受詐騙。

隨著科技的進步，人工智慧在業務的應用預計將不斷擴展，並更加自動化和客製化。人工智慧的數據分析能力將使銀行能夠根據每位客戶的需求和偏好，提供高度個人化的金融產品和服務。

全球業務人工智慧市場動態

影響全球銀行業人工智慧業務的關鍵市場動態是：

關鍵市場促進因素

詐欺偵測和風險管理需求日益成長：隨著金融犯罪日益複雜和頻繁，銀行正轉向人工智慧解決方案來即時偵測詐欺活動。人工智慧能夠分析大量交易資料、發現模式並標記異常，使其成為降低風險的重要工具。

透過個人化提升客戶體驗：人工智慧 (AI) 在提升銀行業客戶服務方面發揮關鍵作用。借助人工智慧聊天機器人、虛擬助理和個人化提案，銀行可以為消費者提供量身定做的解決方案。銀行可以利用人工智慧監控消費者的行為、偏好和交易歷史，從而客製化滿足個人需求的金融產品和服務。

提高業務效率並降低成本：人工智慧技術透過自動化貸款申請處理、文件驗證和客戶服務等常規和重複流程來幫助銀行。自動化減少了人機互動的需求，加快了流程速度並降低了出錯的可能性。透過簡化流程，人工智慧降低了營運成本，使銀行能夠更有效地分配資源，專注於更高價值的業務。

主要問題

資料隱私與安全性隨著銀行擴大機會人工智慧分析大量客戶數據，保護這些敏感資訊的隱私和安全至關重要。遵守歐洲《一般資料保護規則》(GDPR) 和美國《加州消費者隱私法案》(CCPA) 等法規是一項重大挑戰。

與舊有系統整合：許多銀行使用的舊有系統與現代人工智慧技術不相容。將人工智慧解決方案整合到過時的基礎設施中可能非常複雜、成本高昂業務。

人才短缺：人工智慧技術的快速發展需要資料科學、機器學習和人工智慧應用的人才。然而，這些領域存在嚴重的人才短缺，導致銀行難以找到並留住優秀員工。這種人才缺口可能會阻礙人工智慧的成功應用和營運，從而限制銀行有效利用人工智慧的能力。

主要趨勢

提升客戶體驗：銀行擴大利用人工智慧來提供個人化的客戶體驗。人工智慧聊天機器人和虛擬助理正被用於提供全天候客戶服務，輕鬆處理客戶諮詢和交易。透過評估客戶數據，銀行可以個人化提案和服務，從而提高客戶滿意度和忠誠度。

詐欺偵測與預防：隨著網路風險的不斷演變，人工智慧技術在改善銀行安全程序方面發揮著日益重要的作用。機器學習演算法可以即時評估交易模式，並偵測可能存在詐欺行為的異常行為。透過自動化詐欺偵測，銀行可以更快應對潛在威脅，限制財務損失，並維護客戶信心。

風險管理與合規：人工智慧正在透過更精準的風險評估，改變銀行的風險管理業務。銀行可以利用先進的分析和預測模型來識別貸款、投資和監管合規的潛在風險。

目錄

第1章 全球業務人工智慧市場應用情況

• 市場概覽
• 研究範圍
• 先決條件

第2章執行摘要

第3章：已驗證的市場研究調查方法

• 資料探勘
• 驗證
• 第一手資料
• 資料來源列表

第4章 全球業務人工智慧市場展望

• 概述
• 市場動態
  • 驅動程式
  • 限制因素
  • 機會
• 波特五力模型
• 價值鏈分析

第5章全球銀行業人工智慧市場（按產品）

• 概述
• 硬體
• 軟體
• 服務

第6章 全球業務人工智慧應用市場

• 概述
• 分析
• 聊天機器人
• 機器人流程自動化

7. 全球業務人工智慧市場（按地區）

• 概述
• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲
  • 德國
  • 英國
  • 法國
  • 義大利
  • 西班牙
  • 其他歐洲國家
• 亞太地區
  • 中國
  • 日本
  • 印度
  • 其他亞太地區
• 其他
  • 拉丁美洲
  • 中東和非洲

8. 全球業務人工智慧市場競爭格局

• 概述
• 各公司市場排名
• 重點發展策略

第9章 公司簡介

• 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10章 重大進展

• 產品發布/開發
• 合併與收購
• 業務擴展
• 夥伴關係與合作

第11章 附錄

• 相關調查

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