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市场调查报告书
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1797903

全球人工智慧模型风险管理市场：未来预测（至 2032 年）—按产品、部署方法、风险类型、应用、最终用户和地区进行分析

AI Model Risk Management Market Forecasts to 2032 - Global Analysis By Offering (Software and Services), Deployment Model (On-premise, Cloud-based and Hybrid), Risk Type, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球人工智慧模型风险管理市场预计在 2025 年达到 65.4 亿美元，到 2032 年将达到 173.1 亿美元，预测期内的复合年增长率为 14.9%。

用于识别、评估、追踪和降低与人工智慧模型的创建、应用和部署相关的风险的流程、框架和控制措施统称为人工智慧模型风险管理 (AI MRM)。这些风险包括运行故障、偏差、过度拟合、解释不足、资料品质问题以及法规不合规。全面的模型检验、持续的性能监控、模型设计和假设的记录、边缘案例的压力测试以及建立管治框架以确保课责，这些都是有效实施 AI MRM 的必要条件。透过主动管理这些风险，组织可以提高模型的可靠性，建立信任，并遵守不断变化的法律和道德要求。

根据美国标准与技术研究院（NIST）介绍，人工智慧风险管理框架（AI RMF）历时18个月，透过透明、多利益相关方参与的相关利益者製定而成，涉及来自工业界、学术界、民间社会和政府的240多个组织，旨在建立一个自愿、灵活的资源，以在所有领域和用例中培育值得信赖和负责任的人工智慧。

各行各业对人工智慧的采用

人工智慧不再局限于科技巨头或专业使用案例，製造业、物流业、零售业、公共安全、教育业甚至农业等产业都迅速普及。每个行业对风险管理和合规性都有不同的要求。此外，例如，FDA 已提案有关医疗设备人工智慧的规则，要求对持续学习系统进行持续检验。国家道路安全法规规定，自动驾驶汽车中使用的人工智慧 (AI) 必须通过安全性和可靠性测试。随着越来越多的行业寻求专门的管治框架来应对其独特的营运风险，该行业的这种扩展通过增加需要 AI MRM 功能的组织数量来推动市场成长。

缺乏合格的专业人员

人工智慧风险风险管理 (AI MRM) 是一个相对较新的领域，它将人工智慧的技术知识与网路安全、道德、风险管治和法规遵循的专业知识相结合。这种技能的交叉融合非常罕见，造成了人才瓶颈。世界经济论坛指出，对人工智慧相关工作的需求正在快速成长，但人工智慧管治专家的人才库却未能跟上脚步。此外，设计、实施和维护人工智慧风险风险管理 (AI MRM) 系统的专业知识不足，阻碍了组织成功实施治理框架的能力。这种短缺可能导致管治、监控不平衡，以及对不考虑人工智慧特定风险的通用风险管理技术的依赖。

建构人工智慧专业管治平台

将管治、风险评估和合规性报告功能与 AI 模型生命週期管理相结合的专用平台市场正在不断成长。与传统的 GRC 软体相比，AI MRM 平台解决了 AI 特有的问题，例如可解释性、偏差检测、对抗性攻击预防以及持续学习模型的追踪。根据云端安全联盟 (CSA) 的说法，资料表、模型卡和风险登记册应该已经成为企业工作流程的一部分。此外，大规模部署 AI 的企业可能会发现，将这些功能整合到整合仪表板中的新兴企业和成熟的 GRC 提供者是关键的基础设施。

依赖自动化 MRM 工具的危险

随着人工智慧支援的 MRM 软体的不断发展，企业可能会认为自动化合规仪錶板将完全取代人工监管。人工智慧伙伴关係关係组织和欧盟委员会强调，相关人员的参与、伦理考量和背景风险评估仍需要人工判断。如果自动化 MRM 工具遗漏了重要的风险，过度依赖它们可能会提供虚假的安全性和合规性保证，使组织面临业务失败和监管处罚的风险。

COVID-19的影响：

新冠疫情对人工智慧模型风险管理 (AI MRM) 市场产生了双重影响。应对疫情相关问题的组织（例如供应链优化、医疗诊断、救援工作中的欺诈检测以及远端客户支援）迅速采用人工智慧，这往往超过了全面测试和管治的速度，从而增加了偏见、错误和模型漂移的风险。人工智慧使用的激增凸显了对强大的 MRM 框架的需求，以确保紧急情况下的可靠性。此外，经合组织 (OECD) 和美国国家标准与技术研究院 (NIST) 等监管机构和行业协会已开始强调弹性、透明度和持续监控是负责任的人工智慧的关键要素，这进一步推动了后疫情时代对人工智慧 MRM 解决方案的需求。

预计预测期内模型风险部分最大

预计模型风险部分将在预测期内占据最大的市场占有率。这种主导地位归因于 AI MRM 框架主要旨在解决特定于模型的风险，例如偏差、过度拟合、缺乏可解释性、数据品质问题以及性能随时间推移而下降。模型风险管理在银行、保险和医疗保健等领域至关重要，这些领域的 AI 模型直接影响关键决策，例如信贷核准、诈欺侦测和诊断建议。此外，模型检验、针对边缘情况的测试、记录假设以及定期监控产出都在 NIST AI 风险管理框架和巴塞尔委员会的模型风险管治原则等监管框架中受到高度重视。

预计诈欺侦测和风险缓解部门在预测期内将以最高复合年增长率成长

预计诈欺侦测和风险缓解领域将在预测期内实现最高成长率。该领域的快速成长源于诈欺活动日益复杂化，尤其是在银行、金融科技、保险和电子商务领域，这需要能够即时识别异常的先进人工智慧系统。随着诈骗手段的演变，企业正在使用具有持续学习能力的人工智慧模型来发现细微的模式，并防止财务和声誉损失。此外，这些模型必须在严格的风险管治下运行，以保持客观性和可解释性，并遵守美国《银行保密法》、欧盟《人工智慧法》和《洗钱防制》等法律。

占比最大的地区：

预计北美将在预测期内占据最大的市场占有率，这得益于该地区强大的监管框架、早期采用人工智慧技术以及关键技术公司、金融机构和人工智慧管治解决方案提供商的存在。美国联邦在这​​方面处于世界领先地位，这得益于监督(FRB)、美国监理署 (OCC) 和国家标准与技术研究院 (NIST) 等组织的严格合规要求，这些要求要求强有力的模型检验、监控和管治实践。此外，人工智慧在银行、医疗保健和政府服务的快速整合推动了对全面风险管理框架的需求。加拿大的人工智慧道德和透明度措施进一步推动了市场扩张。

复合年增长率最高的地区：

预计亚太地区将在预测期内实现最高的复合年增长率，这得益于数位转型步伐的加快，政府、银行、製造业和医疗保健领域对人工智慧的日益普及，以及监管机构对负责任人工智慧的日益重视。除了对人工智慧基础设施的大量投资外，中国、印度、新加坡和日本等国家也在采用框架和指南来解决模型管治、演算法偏差和资料隐私问题。此外，政府支持的人工智慧倡议，例如新加坡的《人工智慧管治框架》和印度的国家人工智慧策略，正在为长期市场扩张奠定坚实基础，使亚太地区成为该领域成长最快的地区。

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目录

第一章执行摘要

第 2 章 简介

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 主要研究资料
    • 二手研究资讯来源
    • 先决条件

第三章市场走势分析

  • 介绍
  • 驱动程式
  • 抑制因素
  • 市场机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球人工智慧模型风险管理市场（按产品提供）

  • 软体
    • 模型管理
    • 偏差检测
    • 可解释的人工智慧工具
  • 服务
    • 专业服务
    • 託管服务

6. 全球人工智慧模型风险管理市场（按部署方法）

  • 本地
  • 云端基础
  • 杂交种

7. 全球人工智慧模型风险管理市场（按风险类型）

  • 模型风险
  • 操作风险
  • 合规风险
  • 声誉风险
  • 策略风险
  • 道德风险

8. 全球人工智慧模型风险管理市场（按应用）

  • 诈欺侦测和风险降低
  • 资料分类和标记
  • 情绪分析
  • 模型库存管理
  • 客户分类与定位
  • 监理合规监控
  • 其他用途

9. 全球人工智慧模型风险管理市场（按最终用户）

  • 银行、金融服务和保险（BFSI）
  • 零售与电子商务
  • 资讯科技/通讯
  • 製造业
  • 医学与生命科​​学
  • 媒体与娱乐
  • 政府/公共部门
  • 其他最终用户

第 10 章：全球人工智慧模型风险管理市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併与收购（M&A）
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章：企业概况

  • Microsoft
  • Google
  • LogicGate Inc
  • Amazon Web Services（AWS）
  • IBM Corporation
  • H2O.ai
  • SAS Institute
  • Alteryx
  • UpGuard Inc
  • DataRobot, Inc.
  • MathWorks Inc
  • ComplyCube
  • BigID
  • Holistic AI
  • ValidMind Inc
Product Code: SMRC30273

According to Stratistics MRC, the Global AI Model Risk Management Market is accounted for $6.54 billion in 2025 and is expected to reach $17.31 billion by 2032 growing at a CAGR of 14.9% during the forecast period. The processes, frameworks, and controls used to identify, evaluate, track, and reduce risks related to the creation, application, and deployment of artificial intelligence models are collectively referred to as AI Model Risk Management (AI MRM). These risks may include operational failures, bias, overfitting, a lack of explanation, problems with data quality, and non-compliance with regulations. Thorough model validation, ongoing performance monitoring, model design and assumption documentation, edge case stress testing, and the establishment of governance frameworks to guarantee accountability are all necessary for effective AI MRM. Organizations can improve model reliability, foster trust, and adhere to changing legal and ethical requirements by proactively managing these risks.

According to the National Institute of Standards and Technology (NIST), the AI Risk Management Framework (AI RMF) was developed over 18 months through a transparent, multi-stakeholder process involving more than 240 organizations-spanning industry, academia, civil society, and government-to establish a voluntary, flexible resource that fosters trustworthy and responsible AI across all sectors and use cases.

Market Dynamics:

Driver:

AI adoption across industries

AI is being quickly implemented in industries like manufacturing, logistics, retail, public safety, education, and even agriculture; it is no longer limited to tech giants or specialized use cases. Every one of these sectors has distinct requirements for risk management and compliance. Moreover, the FDA, for instance, has proposed rules for AI in medical devices that call for ongoing revalidation of continuous learning systems. According to national road safety regulations, artificial intelligence (AI) used in autonomous vehicles must pass safety and reliability testing. As more industries look for specialized governance frameworks that address their unique operational risks, the number of organizations that require AI MRM capabilities increases due to this sectoral expansion, propelling market growth.

Restraint:

Lack of qualified professionals

AI MRM is a relatively new field that combines technical AI knowledge with expertise in cybersecurity, ethics, risk governance, and regulatory compliance. There is a talent bottleneck because this skill intersection is uncommon. The demand for AI-related jobs is increasing quickly, but the talent pool for AI governance experts is not keeping up, according to the World Economic Forum. Additionally, insufficient expertise in AI MRM system design, implementation, and maintenance hinders organizations' ability to successfully operationalize governance frameworks. Due to this shortage, there are delays, uneven monitoring, and occasionally a dependence on general risk management techniques that do not take into account the risks unique to AI.

Opportunity:

Creation of governance platforms particular to AI

A growing market exists for specialized platforms that combine governance, risk assessment, and compliance reporting capabilities with AI model lifecycle management. In contrast to conventional GRC software, AI MRM platforms would handle AI-specific issues like explainability, bias detection, preventing adversarial attacks, and tracking continuous learning models. Data sheets, model cards, and risk registers should already be part of enterprise workflows, according to the Cloud Security Alliance (CSA). Furthermore, businesses implementing AI at scale may find that startups and well-established GRC providers who incorporate these features into unified dashboards can serve as vital infrastructure.

Threat:

Danger of dependence on automated MRM tools

As AI MRM software advances, companies run the risk of considering automated compliance dashboards to be a full replacement for human oversight. The Partnership on AI and the European Commission has emphasized that stakeholder engagement, ethical considerations, and contextual risk assessment still require human judgment. In the event that automated MRM tools overlook important risks, an over-reliance on them could lead to false assurances of safety or compliance, leaving organizations open to operational failures and regulatory penalties.

Covid-19 Impact:

The COVID-19 pandemic affected the market for AI Model Risk Management (AI MRM) in two ways: it highlighted governance flaws and accelerated adoption. Rapid AI deployment by organizations to tackle pandemic-related issues, including supply chain optimization, healthcare diagnostics, and fraud detection in relief efforts, and remote customer support, frequently outpaced thorough testing and governance, increasing the risk of bias, errors, and model drift. The need for strong MRM frameworks to guarantee dependability in emergency situations was highlighted by this spike in AI use, particularly since unstable market conditions made predictive models less reliable. Moreover, the post-pandemic demand for AI MRM solutions was further fuelled by regulatory agencies and industry associations, such as the OECD and NIST, which started highlighting resilience, transparency, and continuous monitoring as crucial elements of responsible AI.

The model risk segment is expected to be the largest during the forecast period

The model risk segment is expected to account for the largest market share during the forecast period. This dominance results from AI MRM frameworks' primary goal of addressing model-specific risks, including bias, overfitting, lack of explainability, problems with data quality, and performance degradation over time. In sectors like banking, insurance, and healthcare, where AI models have a direct impact on crucial choices like credit approvals, fraud detection, and diagnostic recommendations, model risk management is essential. Additionally, validating models, testing against edge cases, recording assumptions, and regularly monitoring outputs are all highly valued in regulatory frameworks, such as the NIST AI Risk Management Framework and the Basel Committee's principles for model risk governance.

The fraud detection and risk reduction segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the fraud detection and risk reduction segment is predicted to witness the highest growth rate. The increasing sophistication of fraud schemes, especially in banking, fintech, insurance, and e-commerce, which necessitate sophisticated AI systems that can identify anomalies in real time, is driving this segment's rapid growth. Organizations are using AI models with continuous learning capabilities to spot subtle patterns and stop financial and reputational losses as fraud tactics change. Furthermore, to maintain objectivity, explainability, and compliance with laws like the U.S. Bank Secrecy Act, the EU AI Act, and anti-money laundering (AML) directives, these models must, nevertheless, function under stringent risk governance.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the region's robust regulatory framework, early AI technology adoption, and the existence of significant technology firms, financial institutions, and providers of AI governance solutions. Because of strict compliance requirements from organizations like the Federal Reserve, the Office of the Comptroller of the Currency (OCC), and the National Institute of Standards and Technology (NIST), which demand strong model validation, monitoring, and governance practices, the United States leads the world in this regard. Furthermore, the need for thorough risk management frameworks has increased due to the quick integration of AI in banking, healthcare, and government services; further supporting market expansion are Canada's AI ethics and transparency initiatives.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by the quickening pace of digital transformation, the growing use of AI in the government, banking, manufacturing, and healthcare sectors, as well as the growing emphasis on responsible AI by regulators. In addition to making significant investments in AI infrastructure, nations like China, India, Singapore, and Japan are also implementing frameworks and guidelines to address model governance, algorithmic bias, and data privacy. Moreover, Asia-Pacific is the fastest-growing region in this field because of government-backed AI initiatives like Singapore's AI Governance Framework and India's National AI Strategy, which are laying a solid basis for long-term market expansion.

Key players in the market

Some of the key players in AI Model Risk Management Market include Microsoft, Google, LogicGate Inc, Amazon Web Services (AWS), IBM Corporation, H2O.ai, SAS Institute, Alteryx, UpGuard Inc, DataRobot, Inc., MathWorks Inc, ComplyCube, BigID, Holistic AI and ValidMind Inc.

Key Developments:

In August 2025, Cloud services giant Amazon Web Services (AWS) and Malaysian clean energy solutions provider Gentari have signed a power purchase agreement (PPA) for an 80MW wind power project in Tamil Nadu, India, a state on the south-eastern coast of the Indian peninsula.

In July 2025, Alphabet Inc.'s Google inked a deal worth more than $1 billion to provide cloud-computing services to software firm ServiceNow Inc., a win for Google Cloud's efforts to get major enterprises onto its platform. ServiceNow committed to spending $1.2 billion over five years, according to a person familiar with the agreement who asked not to be identified discussing internal information.

In July 2025, Microsoft has achieved a breakthrough with CISPE, the European cloud organization. After years of negotiations, an agreement has been reached on better licensing terms for European cloud providers. The agreement aims to strengthen competition and support European digital sovereignty.

Offerings Covered:

  • Software
  • Services

Deployment Models Covered:

  • On-premise
  • Cloud-based
  • Hybrid

Risk Types Covered:

  • Model Risk
  • Operational Risk
  • Compliance Risk
  • Reputational Risk
  • Strategic Risk
  • Ethical Risk

Applications Covered:

  • Fraud Detection and Risk Reduction
  • Data Classification and Labelling
  • Sentiment Analysis
  • Model Inventory Management
  • Customer Segmentation and Targeting
  • Regulatory Compliance Monitoring
  • Other Applications

End Users Covered:

  • Banking, Financial Services, And Insurance (BFSI)
  • Retail & E-commerce
  • IT & Telecom
  • Manufacturing
  • Healthcare & Life Sciences
  • Media & Entertainment
  • Government and Public Sector
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI Model Risk Management Market, By Offering

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Model Management
    • 5.2.2 Bias Detection
    • 5.2.3 Explainable AI Tools
  • 5.3 Services
    • 5.3.1 Professional Services
    • 5.3.2 Managed Services

6 Global AI Model Risk Management Market, By Deployment Model

  • 6.1 Introduction
  • 6.2 On-premise
  • 6.3 Cloud-based
  • 6.4 Hybrid

7 Global AI Model Risk Management Market, By Risk Type

  • 7.1 Introduction
  • 7.2 Model Risk
  • 7.3 Operational Risk
  • 7.4 Compliance Risk
  • 7.5 Reputational Risk
  • 7.6 Strategic Risk
  • 7.7 Ethical Risk

8 Global AI Model Risk Management Market, By Application

  • 8.1 Introduction
  • 8.2 Fraud Detection and Risk Reduction
  • 8.3 Data Classification and Labelling
  • 8.4 Sentiment Analysis
  • 8.5 Model Inventory Management
  • 8.6 Customer Segmentation and Targeting
  • 8.7 Regulatory Compliance Monitoring
  • 8.8 Other Applications

9 Global AI Model Risk Management Market, By End User

  • 9.1 Introduction
  • 9.2 Banking, Financial Services, And Insurance (BFSI)
  • 9.3 Retail & E-commerce
  • 9.4 IT & Telecom
  • 9.5 Manufacturing
  • 9.6 Healthcare & Life Sciences
  • 9.7 Media & Entertainment
  • 9.8 Government and Public Sector
  • 9.9 Other End Users

10 Global AI Model Risk Management Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Microsoft
  • 12.2 Google
  • 12.3 LogicGate Inc
  • 12.4 Amazon Web Services (AWS)
  • 12.5 IBM Corporation
  • 12.6 H2O.ai
  • 12.7 SAS Institute
  • 12.8 Alteryx
  • 12.9 UpGuard Inc
  • 12.10 DataRobot, Inc.
  • 12.11 MathWorks Inc
  • 12.12 ComplyCube
  • 12.13 BigID
  • 12.14 Holistic AI
  • 12.15 ValidMind Inc

List of Tables

  • Table 1 Global AI Model Risk Management Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Model Risk Management Market Outlook, By Offering (2024-2032) ($MN)
  • Table 3 Global AI Model Risk Management Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global AI Model Risk Management Market Outlook, By Model Management (2024-2032) ($MN)
  • Table 5 Global AI Model Risk Management Market Outlook, By Bias Detection (2024-2032) ($MN)
  • Table 6 Global AI Model Risk Management Market Outlook, By Explainable AI Tools (2024-2032) ($MN)
  • Table 7 Global AI Model Risk Management Market Outlook, By Services (2024-2032) ($MN)
  • Table 8 Global AI Model Risk Management Market Outlook, By Professional Services (2024-2032) ($MN)
  • Table 9 Global AI Model Risk Management Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 10 Global AI Model Risk Management Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 11 Global AI Model Risk Management Market Outlook, By On-premise (2024-2032) ($MN)
  • Table 12 Global AI Model Risk Management Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 13 Global AI Model Risk Management Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 14 Global AI Model Risk Management Market Outlook, By Risk Type (2024-2032) ($MN)
  • Table 15 Global AI Model Risk Management Market Outlook, By Model Risk (2024-2032) ($MN)
  • Table 16 Global AI Model Risk Management Market Outlook, By Operational Risk (2024-2032) ($MN)
  • Table 17 Global AI Model Risk Management Market Outlook, By Compliance Risk (2024-2032) ($MN)
  • Table 18 Global AI Model Risk Management Market Outlook, By Reputational Risk (2024-2032) ($MN)
  • Table 19 Global AI Model Risk Management Market Outlook, By Strategic Risk (2024-2032) ($MN)
  • Table 20 Global AI Model Risk Management Market Outlook, By Ethical Risk (2024-2032) ($MN)
  • Table 21 Global AI Model Risk Management Market Outlook, By Application (2024-2032) ($MN)
  • Table 22 Global AI Model Risk Management Market Outlook, By Fraud Detection and Risk Reduction (2024-2032) ($MN)
  • Table 23 Global AI Model Risk Management Market Outlook, By Data Classification and Labelling (2024-2032) ($MN)
  • Table 24 Global AI Model Risk Management Market Outlook, By Sentiment Analysis (2024-2032) ($MN)
  • Table 25 Global AI Model Risk Management Market Outlook, By Model Inventory Management (2024-2032) ($MN)
  • Table 26 Global AI Model Risk Management Market Outlook, By Customer Segmentation and Targeting (2024-2032) ($MN)
  • Table 27 Global AI Model Risk Management Market Outlook, By Regulatory Compliance Monitoring (2024-2032) ($MN)
  • Table 28 Global AI Model Risk Management Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 29 Global AI Model Risk Management Market Outlook, By End User (2024-2032) ($MN)
  • Table 30 Global AI Model Risk Management Market Outlook, By Banking, Financial Services, And Insurance (BFSI) (2024-2032) ($MN)
  • Table 31 Global AI Model Risk Management Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 32 Global AI Model Risk Management Market Outlook, By IT & Telecom (2024-2032) ($MN)
  • Table 33 Global AI Model Risk Management Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 34 Global AI Model Risk Management Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 35 Global AI Model Risk Management Market Outlook, By Media & Entertainment (2024-2032) ($MN)
  • Table 36 Global AI Model Risk Management Market Outlook, By Government and Public Sector (2024-2032) ($MN)
  • Table 37 Global AI Model Risk Management Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.