金融科技领域生成式人工智慧市场－全球产业规模、份额、趋势、机会及预测（按组件、部署、应用、地区和竞争格局划分，2021-2031年）

全球金融科技领域的生成式人工智慧市场预计将从 2025 年的 17.7 亿美元大幅成长到 2031 年的 63.3 亿美元，复合年增长率为 23.66%。

在此背景下，生成式人工智慧指的是应用深度学习架构（尤其是大规模语言模型）来产生原创程式码、内容和资料的技术，这些技术能够简化复杂的金融工作流程并改善决策流程。市场的主要驱动力是营运效率的重要性，因为金融机构正在寻求自动化资源彙整密集任务，例如监管报告、风险建模和诈欺检测。此外，对高度个人化的需求也在推动市场扩张，因为企业希望透过大规模客製化客户互动和投资建议来提高客户维繫。英国金融协会的报告也印证了这个趋势：到2024年，金融机构将把平均12%的技术预算分配给生成式人工智慧，显示他们致力于将这些能力融入其核心营运中。

儘管发展迅速，但市场在监管合规和资料隐私方面仍面临许多重大障碍。某些演算法模型缺乏透明度，难以满足金融监管机构严格的可解释性标准；涉及敏感客户资讯的资料外洩风险仍然是金融机构的主要担忧。因此，如何在复杂多变的全球监管环境中确保金融资料的安全性和准确性，构成了巨大的挑战，并有可能减缓企业界对该技术的采用。

市场驱动因素

随着金融机构努力应对日益复杂的网路威胁，对先进风险管理和诈欺侦测的需求正在从根本上改变市场格局。生成式人工智慧模型正被用于即时筛选大量交易资料集，从而识别出传统规则系统难以侦测的细微诈欺模式。这项技术不仅增强了安全性，还能更准确地区分合法行为和真实风险，进而提高营运效率。为了凸显这项影响，万事达卡在其2024年5月的新闻稿《万事达卡利用生成式人工智慧技术加速信用卡诈欺侦测》中指出，这些预测工具的实施使其全球网路中的诈欺信用卡侦测率翻了一番。

同时，对高度个人化客户体验日益增长的需求正推动这些工具的广泛应用，从而实现大规模的客户互动客製化。金融机构正利用生成模型整合行为数据和个人交易历史，以提供即时、量身定制的投资建议和响应迅速的虚拟支援。对于寻求提升客户参与的公司而言，这项功能已成为重中之重。 NVIDIA 于 2025 年 2 月发布的《金融服务业人工智慧现况》报告预测，生成式人工智慧在客户体验和互动中的应用比例将达到 60%，较前一年显着成长。预计其对财务的影响也将十分显着。花旗集团全球展望与解决方案于 2024 年 6 月发布的报告《金融领域的人工智慧：机器人、银行及其他》指出，到 2028 年，这些技术的成功整合有望为全球银行业带来约 1,700 亿美元的利润成长。

市场挑战

全球金融科技领域生成式人工智慧市场成长面临的主要障碍是演算法不透明、监管合规和资料隐私挑战交织而成的复杂网路。金融机构必须在严格的框架内运营，这些框架要求透明度和对敏感客户资料的严格保护。然而，许多生成式模型固有的「黑箱」特性使得追踪特定金融咨询或结论的推导过程变得复杂，直接与全球监管机构强制执行的可解释性标准相衝突。这种矛盾迫使机构将这些技术的部署限制在低风险的后勤部门环境中，而不是市场扩张潜力最大的高价值面向客户的管道。

因此，监管的模糊性严重限制了创新的广泛应用。对违规和资料外洩的担忧迫使企业采取高度谨慎的策略，实际上扼杀了这些工具的商业性化规模。为了因应这些新风险，根据国际金融协会（IIF）2024年的数据，81%的金融机构将把生成式人工智慧的使用限制在内部的、非面向客户的应用中。这种防御姿态阻碍了市场充分挖掘高度个人化金融服务带来的产生收入机会。

市场趋势

采用合成资料进行隐私保护模型训练正迅速成为应对产业监管和资料隐私挑战的关键解决方案。金融机构越来越多地使用生成演算法来产生人工资料集，这些资料集在统计上能够复製真实世界的交易细节，但不包括个人识别资讯 (PII)。这种调查方法使银行能够基于各种场景（例如经济衰退和罕见诈欺模式）开发强大的机器学习模型，同时确保严格遵守资料居住法规和隐私法，例如 GDPR。这一趋势正在推动安全协作的新时代。例如，在 2024 年 5 月题为「SWIFT 和全球银行启动人工智慧试点计画以打击跨境支付诈骗」的新闻稿中，SWIFT 宣布该合作组织已联合 10 家主要金融机构，在共用的匿名数据上测试先进的人工智慧，这标誌着向尊重数据主权的集体智慧的重大转变。

同时，市场分析和财务报告的自动化产生正在彻底改变合规负责人和分析师的工作环境。生成式人工智慧工具超越了简单的文字处理，能够独立撰写复杂的文檔，例如投资研究报告、监管文件和获利摘要，从而减轻资料整合的人工负担。这项功能使专业人员能够专注于高价值的策略解读，而不是繁琐的总结工作，显着缩短咨询服务和金融产品的上市时间。这对员工生产力的潜在影响巨大。汤森路透于2024年7月发布的《2024年专业人士未来展望报告》预测，未来五年内，这些人工智慧功能的整合将为行业专业人士每週节省约12小时，从根本上重塑金融公司的资源配置。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章 全球金融科技领域生成式人工智慧市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 按组件（服务、软体）
  • 依部署类型（本机部署、云端部署）
  • 按应用领域划分（合规/诈欺侦测、个人助理、资产管理、预测分析、保险、商业分析/报告、客户行为分析等）
  • 按地区
  • 按公司（2025 年）
• 市场地图

6. 北美金融科技领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 我们
  • 加拿大
  • 墨西哥

7. 欧洲金融科技领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

8. 亚太地区金融科技领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

9. 中东和非洲金融科技领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

10. 南美洲金融科技领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章 全球金融科技领域生成式人工智慧市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• IBM Corporation
• Microsoft Corporation
• Google LLC
• NVIDIA Corporation
• Amazon Web Services, Inc.
• Salesforce, Inc.
• Oracle Corporation
• SAP SE
• Palantir Technologies Inc.
• H2O.ai, Inc.

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Generative AI in Fintech Market is projected to experience substantial growth, rising from USD 1.77 Billion in 2025 to USD 6.33 Billion by 2031, representing a compound annual growth rate of 23.66%. In this context, generative AI involves the application of deep learning architectures, specifically large language models, to create original code, content, and data that streamline intricate financial workflows and improve decision-making processes. The market is largely driven by a critical need for operational efficiency, as institutions aim to automate resource-heavy tasks such as regulatory reporting, risk modeling, and fraud detection. Furthermore, the push for hyper-personalization fuels market expansion, enabling entities to customize client interactions and investment advice at scale to boost retention. Reinforcing this trend, UK Finance reported in 2024 that financial institutions allocated an average of 12 percent of their total technology budgets specifically to generative AI, indicating a strong commitment to embedding these capabilities into core operations.

Despite this rapid progress, the market encounters significant obstacles related to regulatory compliance and data privacy. The lack of transparency in certain algorithmic models poses challenges in satisfying the strict explainability standards mandated by financial regulators, while the potential for data leakage regarding sensitive client information remains a major concern for institutions. Consequently, the task of navigating a complex and shifting global regulatory landscape without compromising the security and accuracy of financial data presents a formidable barrier that threatens to decelerate widespread adoption across the enterprise sector.

Market Driver

The growing necessity for advanced risk management and fraud detection is fundamentally transforming the market as financial organizations strive to combat increasingly complex cyber threats. Generative AI models are being utilized to scrutinize immense transaction datasets in real-time, enabling the identification of subtle fraudulent patterns that often escape detection by conventional rule-based systems. This technology not only bolsters security but also enhances operational efficiency by more accurately differentiating between legitimate actions and genuine risks. Highlighting this impact, Mastercard revealed in a May 2024 press release titled "Mastercard accelerates card fraud detection with generative AI technology" that the implementation of these predictive tools allowed the company to double its detection rate of compromised cards across its global network.

Concurrently, the surging demand for hyper-personalized customer experiences is fueling the broad integration of these tools to tailor client interactions on a large scale. Financial institutions are leveraging generative models to synthesize behavioral data and individual transaction histories, facilitating the provision of instant, customized investment guidance and responsive virtual support. This capability has become a central priority for firms seeking to strengthen client engagement; according to the NVIDIA "State of AI in Financial Services" report from February 2025, the utilization of generative AI for customer experience and engagement applications increased to 60 percent, more than doubling the previous year's figures. The financial implications are expected to be significant, with Citi Global Perspectives & Solutions stating in their June 2024 report "AI in Finance: Bot, Bank & Beyond" that successfully integrating these technologies could expand the global banking sector's profit pool by roughly 170 billion dollars by 2028.

Market Challenge

The primary obstacle impeding the growth of the Global Generative AI in Fintech Market is the intricate conflict involving algorithmic opacity, regulatory compliance, and data privacy. Financial entities must operate within rigid frameworks that insist on transparency and the rigorous protection of sensitive client data. However, the intrinsic "black box" nature of many generative models complicates the ability to trace how specific financial advice or conclusions are reached, creating a direct friction with the explainability standards enforced by global regulators. This tension forces organizations to limit the deployment of these technologies to lower-risk back-office environments rather than high-value customer-facing channels where the potential for market expansion is greatest.

As a result, this regulatory ambiguity serves as a severe constraint on widespread innovation. Concerns regarding non-compliance and data leakage drive firms to maintain a highly cautious strategy, effectively stalling the commercial scalability of these tools. Data from the Institute of International Finance (IIF) in 2024 indicates that 81 percent of financial institutions have restricted their use of Generative AI to internal, non-customer-facing applications to manage these emerging risks. This defensive posture prevents the market from fully realizing the revenue-generating opportunities associated with hyper-personalized financial services.

Market Trends

The adoption of synthetic data for privacy-preserving model training is quickly emerging as a vital solution to the sector's regulatory and data privacy challenges. Financial institutions are increasingly employing generative algorithms to produce artificial datasets that statistically replicate real-world transaction details without including personally identifiable information (PII). This methodology allows banks to develop robust machine learning models based on diverse scenarios, such as economic downturns or rare fraud patterns, while strictly adhering to data residency and privacy laws like GDPR. This trend is fostering a new era of secure collaboration; for instance, Swift announced in a May 2024 press release titled "Swift and global banks launch AI pilots to tackle cross-border payments fraud" that the cooperative had gathered 10 leading financial institutions to test advanced AI on anonymously shared data, signaling a major shift toward collective intelligence that respects data sovereignty.

Simultaneously, the automated generation of market insights and financial reports is revolutionizing the operational landscape for compliance officers and analysts. Generative AI tools are advancing beyond simple text processing to independently draft complex documents, such as investment research notes, regulatory filings, and earnings summaries, thereby reducing the manual workload of data synthesis. This functionality enables professionals to concentrate on high-value strategic interpretation instead of routine compilation, which significantly accelerates the time-to-market for advisory services and financial products. The potential impact on workforce productivity is profound; the "2024 Future of Professionals Report" by Thomson Reuters in July 2024 projects that the integration of these AI capabilities will free up approximately 12 hours per week for industry professionals within the next five years, fundamentally reshaping resource allocation in financial firms.

Key Market Players

• IBM Corporation
• Microsoft Corporation
• Google LLC
• NVIDIA Corporation
• Amazon Web Services, Inc.
• Salesforce, Inc.
• Oracle Corporation
• SAP SE
• Palantir Technologies Inc.
• H2O.ai, Inc.

Report Scope

In this report, the Global Generative AI in Fintech Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Generative AI in Fintech Market, By Component

• Services
• Software

Generative AI in Fintech Market, By Deployment

• On-premises
• Cloud

Generative AI in Fintech Market, By Application

• Compliance & Fraud Detection
• Personal Assistants
• Asset Management
• Predictive Analysis
• Insurance
• Business Analytics & Reporting
• Customer Behavioral Analytics
• Others

Generative AI in Fintech Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Generative AI in Fintech Market.

Available Customizations:

Global Generative AI in Fintech Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Generative AI in Fintech Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Component (Services, Software)
  • 5.2.2. By Deployment (On-premises, Cloud)
  • 5.2.3. By Application (Compliance & Fraud Detection, Personal Assistants, Asset Management, Predictive Analysis, Insurance, Business Analytics & Reporting, Customer Behavioral Analytics, Others)
  • 5.2.4. By Region
  • 5.2.5. By Company (2025)
• 5.3. Market Map

6. North America Generative AI in Fintech Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Component
  • 6.2.2. By Deployment
  • 6.2.3. By Application
  • 6.2.4. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Generative AI in Fintech Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Component
      • 6.3.1.2.2. By Deployment
      • 6.3.1.2.3. By Application
  • 6.3.2. Canada Generative AI in Fintech Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Component
      • 6.3.2.2.2. By Deployment
      • 6.3.2.2.3. By Application
  • 6.3.3. Mexico Generative AI in Fintech Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Component
      • 6.3.3.2.2. By Deployment
      • 6.3.3.2.3. By Application

7. Europe Generative AI in Fintech Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component
  • 7.2.2. By Deployment
  • 7.2.3. By Application
  • 7.2.4. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Generative AI in Fintech Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Component
      • 7.3.1.2.2. By Deployment
      • 7.3.1.2.3. By Application
  • 7.3.2. France Generative AI in Fintech Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Component
      • 7.3.2.2.2. By Deployment
      • 7.3.2.2.3. By Application
  • 7.3.3. United Kingdom Generative AI in Fintech Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Component
      • 7.3.3.2.2. By Deployment
      • 7.3.3.2.3. By Application
  • 7.3.4. Italy Generative AI in Fintech Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Component
      • 7.3.4.2.2. By Deployment
      • 7.3.4.2.3. By Application
  • 7.3.5. Spain Generative AI in Fintech Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Component
      • 7.3.5.2.2. By Deployment
      • 7.3.5.2.3. By Application

8. Asia Pacific Generative AI in Fintech Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Deployment
  • 8.2.3. By Application
  • 8.2.4. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Generative AI in Fintech Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Deployment
      • 8.3.1.2.3. By Application
  • 8.3.2. India Generative AI in Fintech Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Deployment
      • 8.3.2.2.3. By Application
  • 8.3.3. Japan Generative AI in Fintech Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Deployment
      • 8.3.3.2.3. By Application
  • 8.3.4. South Korea Generative AI in Fintech Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Component
      • 8.3.4.2.2. By Deployment
      • 8.3.4.2.3. By Application
  • 8.3.5. Australia Generative AI in Fintech Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Component
      • 8.3.5.2.2. By Deployment
      • 8.3.5.2.3. By Application

9. Middle East & Africa Generative AI in Fintech Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Deployment
  • 9.2.3. By Application
  • 9.2.4. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Generative AI in Fintech Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Deployment
      • 9.3.1.2.3. By Application
  • 9.3.2. UAE Generative AI in Fintech Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Deployment
      • 9.3.2.2.3. By Application
  • 9.3.3. South Africa Generative AI in Fintech Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Deployment
      • 9.3.3.2.3. By Application

10. South America Generative AI in Fintech Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Deployment
  • 10.2.3. By Application
  • 10.2.4. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Generative AI in Fintech Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Deployment
      • 10.3.1.2.3. By Application
  • 10.3.2. Colombia Generative AI in Fintech Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Deployment
      • 10.3.2.2.3. By Application
  • 10.3.3. Argentina Generative AI in Fintech Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Deployment
      • 10.3.3.2.3. By Application

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Generative AI in Fintech Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. IBM Corporation
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. Microsoft Corporation
• 15.3. Google LLC
• 15.4. NVIDIA Corporation
• 15.5. Amazon Web Services, Inc.
• 15.6. Salesforce, Inc.
• 15.7. Oracle Corporation
• 15.8. SAP SE
• 15.9. Palantir Technologies Inc.
• 15.10. H2O.ai, Inc.

16. Strategic Recommendations

17. About Us & Disclaimer