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市場調查報告書
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1604528

人工智慧金融市場：未來預測（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個工作天內

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簡介目錄

AI金融市場預計將以16.50%的複合年成長率成長，市場規模從2024年的170.25億美元增至2029年的320.66億美元。

人工智慧金融，也稱為金融人工智慧或金融科技人工智慧，是在金融領域使用人工智慧（AI）技術來促進業務和事實處理、高級選擇、客戶服務等自動化。人工智慧金融採用多種人工智慧策略，包括深度學習、自然語言處理、預測分析和機器人系統自動化。其廣泛的應用領域涵蓋以下產業：銀行、保險、資產管理、經濟科技公司。

人工智慧金融的顯著方面包括自動化金融服務、資料收集和分析以及增強客戶體驗。人工智慧可以透過減少許多手動和重複性任務和流程（例如資料輸入、報表核對、詐欺檢測）來幫助提高財務效率。這種自動化提高了業務效率、降低了成本並最大限度地減少了錯誤。人工智慧演算法分析金融背景下的非常大的資料集，目的是識別相關模式、趨勢和資訊以幫助決策流程。因此，預測分析用於檢測市場、客戶行為和風險的新興趨勢。基於人工智慧的對話代理商或虛擬助理可以幫助客戶提供報價、查詢甚至購買。這是透過 NLP 實現的，它使這些系統能夠即時了解客戶並做出回應，從而提高客戶的整體滿意度。

此外，人工智慧金融是新時代金融科技和服務發展的指南理念，它為經濟主體的商業行為帶來重大變革。隨著人工智慧工具在未來幾年的進步，我們預計金融業將採用更多的人工智慧工具。反過來，這可能會導致金融服務提供的進一步成長和變化。

人工智慧金融市場的驅動力

  • 不斷進步的技術進步正在促進人工智慧金融市場的成長

人工智慧、機器學習和自然語言處理的進步正在增強人工智慧在金融服務中的能力。改進的演算法和模型可以實現更準確的預測、風險評估和個人化的客戶體驗。在市場上提供的各種服務中，SAP Business AI 嵌入到金融應用程式中，以提高生產力、業務洞察力和安全性。自動化活動、提高報告準確性並降低詐欺風險。它還有助於檢測和防止異常情況，使財務專業人員能夠專注於策略目標。

技術進步持續推動金融領域的創新和轉型，人工智慧解決方案可改善決策、業務流程和客戶體驗。隨著人工智慧的進步，業務的未來預計將更加依賴這些技術。

AI金融市場地理版圖

  • 北美在預測期內將經歷指數級成長

除了矽谷、波士頓和西雅圖之外，北美地區還有許多創新中心，其中大部分在美國。可以理解的是，這些地區正在經歷重大活動，因為它們專注於人工智慧開發。其中包括 IBM、 Oracle、Simplifai.ai 和 SAP 等新興企業、大型 IT 公司、研究中心和創業投資，它們都專注於為金融領域創建 AI 解決方案。

北美擁有多元化且監管良好的金融服務業，包括銀行、投資、保險、金融科技以及各種監管機構，包括傳統和自動化機構。該地區發達的金融結構和生態系統有利於人工智慧技術在金融領域各行業的採用。

此外，技術和投資的持續進步、有利的政策以及由於龐大的人才庫而提供的創新公司和人才將推動北美人工智慧金融領域最有益的工具和其他指南。

為什麼要購買這份報告？

  • 富有洞察力的分析：獲得涵蓋主要和新興地區的深入市場洞察，重點關注客戶細分、政府政策和社會經濟因素、消費者偏好、行業明智以及其他子區隔。
  • 競爭格局：了解世界主要企業採取的策略策略，並了解透過正確的策略滲透市場的潛力。
  • 市場促進因素和未來趨勢：探索動態因素和關鍵市場趨勢以及它們將如何塑造未來市場發展。
  • 可行的建議：利用洞察力做出策略決策，以在動態環境中發現新的業務流和收益。
  • 受眾廣泛：對於新興企業、研究機構、顧問、中小企業和大型企業有用且具有成本效益。

它有什麼用？

產業與市場考量、商機評估、產品需求預測、打入市場策略、地理擴張、資本投資決策、法律規範與影響、新產品開發、競爭影響

分析範圍

  • 歷史資料與預測（2022-2029）
  • 成長機會、挑戰、供應鏈前景、法規結構、顧客行為、趨勢分析
  • 競爭對手定位、策略和市場佔有率分析
  • 收益成長率與預測分析：按細分市場/地區（按國家）
  • 公司概況（策略、產品、財務資訊、主要趨勢等）

人工智慧金融市場分為以下幾個部分：

按用途

  • 後勤部門
  • 中台
  • 前台

按用戶

  • 個人理財
  • 消費金融
  • 企業融資

按類型

  • 自然語言處理
  • 大語言模型
  • 情緒分析
  • 影像識別
  • 其他

按地區

  • 北美洲
  • 美國
  • 加拿大
  • 墨西哥
  • 南美洲
  • 巴西
  • 阿根廷
  • 其他
  • 歐洲
  • 德國
  • 法國
  • 英國
  • 西班牙
  • 其他
  • 中東/非洲
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 以色列
  • 其他
  • 亞太地區
  • 中國
  • 日本
  • 印度
  • 韓國
  • 印尼
  • 台灣
  • 其他

目錄

第1章簡介

  • 市場概況
  • 市場定義
  • 分析範圍
  • 市場區隔
  • 貨幣
  • 先決條件
  • 基準年和預測年時間表
  • 相關利益者的主要利益

第2章 分析方法

  • 分析設計
  • 分析過程

第3章執行摘要

  • 主要發現
  • CXO觀點

第4章市場動態

  • 市場促進因素
  • 市場限制因素
  • 波特五力分析
  • 產業價值鏈分析
  • 分析師觀點

第5章 人工智慧金融市場：按應用分類

  • 介紹
  • 後勤部門
  • 中台
  • 前台

第6章 人工智慧金融市場：依使用者分類

  • 介紹
  • 個人理財
  • 消費金融
  • 企業融資

第7章 人工智慧金融市場：按類型

  • 介紹
  • 自然語言處理
  • 大語言模型
  • 情緒分析
  • 影像識別
  • 其他

第8章 人工智慧金融市場：按地區

  • 介紹
  • 北美洲
    • 按用途
    • 按用戶
    • 按類型
    • 按國家/地區
  • 南美洲
    • 按用途
    • 按用戶
    • 按類型
    • 按國家/地區
  • 歐洲
    • 按用途
    • 按用戶
    • 按類型
    • 按國家/地區
  • 中東/非洲
    • 按用途
    • 按用戶
    • 按類型
    • 按國家/地區
  • 亞太地區
    • 按用途
    • 按用戶
    • 按類型
    • 按國家/地區

第9章競爭環境及分析

  • 主要企業及策略分析
  • 市場佔有率分析
  • 企業合併(M&A)、協議與合作
  • 競爭對手儀表板

第10章 公司簡介

  • Oracle
  • IBM
  • Simplifai.ai
  • SAP
  • Walnut AI
  • HP
  • Numerai
  • H2O.ai
  • Nvidia
  • Zeni Inc.
簡介目錄
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.