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市场调查报告书
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1604528

人工智慧金融市场：未来预测（2024-2029）

AI finance market - Forecasts from 2024 to 2029
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 147 Pages | 商品交期: 最快1-2个工作天内

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简介目录

AI金融市场预计将以16.50%的复合年增长率成长，市场规模从2024年的170.25亿美元增至2029年的320.66亿美元。

人工智慧金融，也称为金融人工智慧或金融科技人工智慧，是在金融领域使用人工智慧（AI）技术来促进业务和事实处理、高级选择、客户服务等自动化。人工智慧金融采用多种人工智慧策略，包括深度学习、自然语言处理、预测分析和机器人系统自动化。其广泛的应用领域涵盖以下产业：银行、保险、资产管理、经济科技公司。

人工智慧金融的显着方面包括自动化金融服务、资料收集和分析以及增强客户体验。人工智慧可以透过减少许多手动和重复性任务和流程（例如资料输入、报表核对、诈欺检测）来帮助提高财务效率。这种自动化提高了业务效率、降低了成本并最大限度地减少了错误。人工智慧演算法分析金融背景下的非常大的资料集，目的是识别相关模式、趋势和资讯以帮助决策流程。因此，预测分析用于检测市场、客户行为和风险的新兴趋势。基于人工智慧的对话代理商或虚拟助理可以帮助客户提供报价、查询甚至购买。这是透过 NLP 实现的，它使这些系统能够即时了解客户并做出回应，从而提高客户的整体满意度。

此外，人工智慧金融是新时代金融科技和服务发展的指南理念，它为经济主体的商业行为带来重大变革。随着人工智慧工具在未来几年的进步，我们预计金融业将采用更多的人工智慧工具。反过来，这可能会导致金融服务提供的进一步成长和变化。

人工智慧金融市场的驱动力

  • 不断进步的技术进步正在促进人工智慧金融市场的成长

人工智慧、机器学习和自然语言处理的进步正在增强人工智慧在金融服务中的能力。改进的演算法和模型可以实现更准确的预测、风险评估和个人化的客户体验。在市场上提供的各种服务中，SAP Business AI 嵌入到金融应用程式中，以提高生产力、业务洞察力和安全性。自动化活动、提高报告准确性并降低诈欺风险。它还有助于检测和防止异常情况，使财务专业人员能够专注于策略目标。

技术进步持续推动金融领域的创新和转型，人工智慧解决方案可改善决策、业务流程和客户体验。随着人工智慧的进步，业务的未来预计将更加依赖这些技术。

AI金融市场地理版图

  • 北美在预测期内将经历指数级增长

除了硅谷、波士顿和西雅图之外，北美地区还有许多创新中心，其中大部分在美国。可以理解的是，这些地区正在经历重大活动，因为它们专注于人工智慧开发。其中包括 IBM、 Oracle、Simplifai.ai 和 SAP 等新兴企业、大型 IT 公司、研究中心和创业投资，它们都专注于为金融领域创建 AI 解决方案。

北美拥有多元化且监管良好的金融服务业，包括银行、投资、保险、金融科技以及各种监管机构，包括传统和自动化机构。该地区发达的金融结构和生态系统有利于人工智慧技术在金融领域各行业的采用。

此外，技术和投资的持续进步、有利的政策以及由于庞大的人才库而提供的创新公司和人才将推动北美人工智慧金融领域最有益的工具和其他指南。

为什么要购买这份报告？

  • 富有洞察力的分析：获得涵盖主要和新兴地区的深入市场洞察，重点关注客户细分、政府政策和社会经济因素、消费者偏好、行业明智以及其他子区隔。
  • 竞争格局：了解世界主要企业采取的策略策略，并了解透过正确的策略渗透市场的潜力。
  • 市场驱动因素和未来趋势：探索动态因素和关键市场趋势以及它们将如何塑造未来市场发展。
  • 可行的建议：利用洞察力做出策略决策，以在动态环境中发现新的业务流和收益。
  • 受众广泛：对于新兴企业、研究机构、顾问、中小企业和大型企业有用且具有成本效益。

它有什么用？

产业与市场考量、商机评估、产品需求预测、打入市场策略、地理扩张、资本投资决策、法律规范与影响、新产品开发、竞争影响

分析范围

  • 历史资料与预测（2022-2029）
  • 成长机会、挑战、供应链前景、法规结构、顾客行为、趋势分析
  • 竞争对手定位、策略和市场占有率分析
  • 收益成长率与预测分析：按细分市场/地区（按国家）
  • 公司概况（策略、产品、财务资讯、主要趋势等）

人工智慧金融市场分为以下几个部分：

按用途

  • 后勤部门
  • 中台
  • 前台

按用户

  • 个人理财
  • 消费金融
  • 企业融资

按类型

  • 自然语言处理
  • 大语言模型
  • 情绪分析
  • 影像识别
  • 其他的

按地区

  • 北美洲
  • 美国
  • 加拿大
  • 墨西哥
  • 南美洲
  • 巴西
  • 阿根廷
  • 其他的
  • 欧洲
  • 德国
  • 法国
  • 英国
  • 西班牙
  • 其他的
  • 中东/非洲
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 以色列
  • 其他的
  • 亚太地区
  • 中国
  • 日本
  • 印度
  • 韩国
  • 印尼
  • 台湾
  • 其他的

目录

第一章简介

  • 市场概况
  • 市场定义
  • 分析范围
  • 市场区隔
  • 货币
  • 先决条件
  • 基准年和预测年时间表
  • 相关利益者的主要利益

第二章 分析方法

  • 分析设计
  • 分析过程

第三章执行摘要

  • 主要发现
  • CXO观点

第四章市场动态

  • 市场驱动因素
  • 市场限制因素
  • 波特五力分析
  • 产业价值链分析
  • 分析师观点

第五章 人工智慧金融市场：按应用分类

  • 介绍
  • 后勤部门
  • 中台
  • 前台

第六章 人工智慧金融市场：依使用者划分

  • 介绍
  • 个人理财
  • 消费金融
  • 企业融资

第七章 人工智慧金融市场：按类型

  • 介绍
  • 自然语言处理
  • 大语言模型
  • 情绪分析
  • 影像识别
  • 其他的

第八章 人工智慧金融市场：按地区

  • 介绍
  • 北美洲
    • 按用途
    • 按用户
    • 按类型
    • 按国家/地区
  • 南美洲
    • 按用途
    • 按用户
    • 按类型
    • 按国家/地区
  • 欧洲
    • 按用途
    • 按用户
    • 按类型
    • 按国家/地区
  • 中东/非洲
    • 按用途
    • 按用户
    • 按类型
    • 按国家/地区
  • 亚太地区
    • 按用途
    • 按用户
    • 按类型
    • 按国家/地区

第九章竞争环境及分析

  • 主要企业及策略分析
  • 市场占有率分析
  • 企业合併(M&A)、协议与合作
  • 竞争对手仪表板

第十章 公司简介

  • Oracle
  • IBM
  • Simplifai.ai
  • SAP
  • Walnut AI
  • HP
  • Numerai
  • H2O.ai
  • Nvidia
  • Zeni Inc.
简介目录
Product Code: KSI061616757

The AI finance market is expected to grow at a CAGR of 16.50%, reaching a market size of US$32.066 billion in 2029 from US$17.025 billion in 2024.

AI Finance, referred to as AI in Finance or FinTech AI, is the usage of artificial intelligence (AI) technologies in finance to facilitate automation of duties and facts processing, progressed choice making, and customer service, among others. AI Finance employs diverse AI strategies, which include deep learning, herbal language processing, predictive analytics, and robotics system automation. Its extensive applications encompass the subsequent sectors, banking, insurance, asset management, and economic technology companies.

Some notable aspects of AI finance include the automation of financial services, data collection and analysis, and enhancement of customer experiences. Artificial intelligence helps finance increase the effectiveness of concerns by reducing many manual and repetitive tasks and processes - such as entering data, reconciling statements, carrying out compliance procedures, and spotting fraud. Such automation improves operational efficiency, reduces costs, and minimizes errors. AI algorithms analyze very large-scale datasets in a financial context with the aim of identifying relevant patterns, trends, and information to aid in the decision-making processes. Thus, predictive analytics is used to detect emerging trends within markets, customer behaviors, and risks. AI-based conversational agents and virtual personal assistants help customers with offers, queries, and even purchasing. This is made possible through NLP, which enables these systems to understand and respond to customers in real-time, contributing to their overall satisfaction.

Moreover, AI Finance is a guiding concept in the new era of the development of financial technologies and services since it introduces considerable transformations in the business practices of economic entities. It is expected that advancing AI Tools in the coming years will result in the finance sector adopting more AI tools. This will, in turn, lead to more growth and change dynamics in providing financial services.

AI finance market drivers

  • Rising technological advancements are contributing to the AI finance market growth

Improvements in artificial intelligence, machine learning, and natural language processing have enhanced the functionality of AI within financial services. Improved algorithms and models allow for more accurate forecasts, risk assessments, and personalized client experiences. Among various services available in the market, SAP Business AI is incorporated into finance applications, which improves productivity, business insight, and security. It automates activities, increases reporting accuracy, and lowers fraud risk. It also aids in anomaly discovery and prevention, freeing finance professionals to concentrate on strategic objectives.

Innovation and transitions in the finance sector continue to be propelled by technological advancements, making AI solutions available for improved decision-making, operational processes, and customer experiences. With the evolution of artificial intelligence, it's expected that banking's future will depend on these technologies even more.

AI finance market geographical outlook

  • North America is witnessing exponential growth during the forecast period

Apart from Silicon Valley, Boston, and Seattle, many more centers of technological innovation are located in the North American region, most of which are in the US. It is understandable that these regions experience significant activity due to the emphasis on developing AI. This includes startups, major IT companies, research centers, and venture capital firms like IBM, Oracle, Simplifai.ai, and SAP, all focused on creating AI solutions for the finance sector.

North America has a diverse and well-regulated financial services sector that includes banking, investments, insurance, fintech, and various regulatory authorities, including traditional and automated bodies. The region's well-developed financial structure and ecosystem are favorable to embracing AI technology across various industries within the finance sector.

Moreover, the tools and other guiding factors proving the most beneficial in the field of AI finance in North America will be applicable for a long time owing to the continuous advancement in technology and investment, favorable policies, and the large pool of innovative companies and talent within reach.

Reasons for buying this report:-

  • Insightful Analysis: Gain detailed market insights covering major as well as emerging geographical regions, focusing on customer segments, government policies and socio-economic factors, consumer preferences, industry verticals, other sub- segments.
  • Competitive Landscape: Understand the strategic maneuvers employed by key players globally to understand possible market penetration with the correct strategy.
  • Market Drivers & Future Trends: Explore the dynamic factors and pivotal market trends and how they will shape up future market developments.
  • Actionable Recommendations: Utilize the insights to exercise strategic decision to uncover new business streams and revenues in a dynamic environment.
  • Caters to a Wide Audience: Beneficial and cost-effective for startups, research institutions, consultants, SMEs, and large enterprises.

What do businesses use our reports for?

Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive Intelligence

Report Coverage:

  • Historical data & forecasts from 2022 to 2029
  • Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, Customer Behaviour, and Trend Analysis
  • Competitive Positioning, Strategies, and Market Share Analysis
  • Revenue Growth and Forecast Assessment of segments and regions including countries
  • Company Profiling (Strategies, Products, Financial Information, and Key Developments among others)

The AI finance market is analyzed into the following segments:

By Application

  • Back Office
  • Middle office
  • Front Office

By Users

  • Personal Finance
  • Consumer Finance
  • Corporate Finance

By Type

  • Natural Language Processing
  • Large Language Models
  • Sentiment analysis
  • Image recognition
  • Others

By Geography

  • North America
  • USA
  • Canada
  • Mexico
  • South America
  • Brazil
  • Argentina
  • Others
  • Europe
  • Germany
  • France
  • UK
  • Spain
  • Others
  • Middle East and Africa
  • Saudi Arabia
  • UAE
  • Israel
  • Others
  • Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Indonesia
  • Taiwan
  • Others

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Market Overview
  • 1.2. Market Definition
  • 1.3. Scope of the Study
  • 1.4. Market Segmentation
  • 1.5. Currency
  • 1.6. Assumptions
  • 1.7. Base and Forecast Years Timeline
  • 1.8. Key Benefits to the Stakeholder

2. RESEARCH METHODOLOGY

  • 2.1. Research Design
  • 2.2. Research Processes

3. EXECUTIVE SUMMARY

  • 3.1. Key Findings
  • 3.2. CXO Perspective

4. MARKET DYNAMICS

  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porter's Five Forces Analysis
    • 4.3.1. Bargaining Power of Suppliers
    • 4.3.2. Bargaining Power of Buyers
    • 4.3.3. Threat of New Entrants
    • 4.3.4. Threat of Substitutes
    • 4.3.5. Competitive Rivalry in the Industry
  • 4.4. Industry Value Chain Analysis
  • 4.5. Analyst View

5. AI FINANCE MARKET BY APPLICATION

  • 5.1. Introduction
  • 5.2. Back Office
  • 5.3. Middle office
  • 5.4. Front Office

6. AI FINANCE MARKET BY USERS

  • 6.1. Introduction
  • 6.2. Personal Finance
  • 6.3. Consumer Finance
  • 6.4. Corporate Finance

7. AI FINANCE MARKET BY TYPE

  • 7.1. Introduction
  • 7.2. Natural Language Processing
  • 7.3. Large Language Models
  • 7.4. Sentiment analysis
  • 7.5. Image recognition
  • 7.6. Others

8. AI FINANCE MARKET BY GEOGRAPHY

  • 8.1. Introduction
  • 8.2. North America
    • 8.2.1. By Application
    • 8.2.2. By User
    • 8.2.3. By Type
    • 8.2.4. By Country
      • 8.2.4.1. USA
      • 8.2.4.2. Canada
      • 8.2.4.3. Mexico
  • 8.3. South America
    • 8.3.1. By Application
    • 8.3.2. By User
    • 8.3.3. By Type
    • 8.3.4. By Country
      • 8.3.4.1. Brazil
      • 8.3.4.2. Argentina
      • 8.3.4.3. Others
  • 8.4. Europe
    • 8.4.1. By Application
    • 8.4.2. By User
    • 8.4.3. By Type
    • 8.4.4. By Country
      • 8.4.4.1. Germany
      • 8.4.4.2. France
      • 8.4.4.3. UK
      • 8.4.4.4. Spain
      • 8.4.4.5. Others
  • 8.5. Middle East and Africa
    • 8.5.1. By Application
    • 8.5.2. By User
    • 8.5.3. By Type
    • 8.5.4. By Country
      • 8.5.4.1. Saudi Arabia
      • 8.5.4.2. UAE
      • 8.5.4.3. Others
  • 8.6. Asia Pacific
    • 8.6.1. By Application
    • 8.6.2. By User
    • 8.6.3. By Type
    • 8.6.4. By Country
      • 8.6.4.1. China
      • 8.6.4.2. Japan
      • 8.6.4.3. India
      • 8.6.4.4. South Korea
      • 8.6.4.5. Indonesia
      • 8.6.4.6. Taiwan
      • 8.6.4.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 9.1. Major Players and Strategy Analysis
  • 9.2. Market Share Analysis
  • 9.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 9.4. Competitive Dashboard

10. COMPANY PROFILES

  • 10.1. Oracle
  • 10.2. IBM
  • 10.3. Simplifai.ai
  • 10.4. SAP
  • 10.5. Walnut AI
  • 10.6. HP
  • 10.7. Numerai
  • 10.8. H2O.ai
  • 10.9. Nvidia
  • 10.10. Zeni Inc.