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1797903

全球人工智慧模型風險管理市場：未來預測（至 2032 年）—按產品、部署方法、風險類型、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧模型風險管理市場預計在 2025 年達到 65.4 億美元，到 2032 年將達到 173.1 億美元，預測期內的複合年成長率為 14.9%。

用於識別、評估、追蹤和降低與人工智慧模型的創建、應用和部署相關的風險的流程、框架和控制措施統稱為人工智慧模型風險管理 (AI MRM)。這些風險包括運行故障、偏差、過度擬合、解釋不足、資料品質問題以及法規不合規。全面的模型檢驗、持續的性能監控、模型設計和假設的記錄、邊緣案例的壓力測試以及建立管治框架以確保課責，這些都是有效實施 AI MRM 的必要條件。透過主動管理這些風險，組織可以提高模型的可靠性，建立信任，並遵守不斷變化的法律和道德要求。

根據美國標準與技術研究院（NIST）介紹，人工智慧風險管理框架（AI RMF）歷時18個月，透過透明、多利益相關方參與的相關利益者製定而成，涉及來自工業界、學術界、民間社會和政府的240多個組織，旨在建立一個自願、靈活的資源，以在所有領域和用例中培育值得信賴和負責任的人工智慧。

各行各業對人工智慧的採用

人工智慧不再局限於科技巨頭或專業使用案例，製造業、物流業、零售業、公共安全、教育業甚至農業等產業都迅速普及。每個行業對風險管理和合規性都有不同的要求。此外，例如，FDA 已提案有關醫療設備人工智慧的規則，要求對持續學習系統進行持續檢驗。國家道路安全法規規定，自動駕駛汽車中使用的人工智慧 (AI) 必須通過安全性和可靠性測試。隨著越來越多的行業尋求專門的管治框架來應對其獨特的營運風險，該行業的這種擴展通過增加需要 AI MRM 功能的組織數量來推動市場成長。

缺乏合格的專業人員

人工智慧風險風險管理 (AI MRM) 是一個相對較新的領域，它將人工智慧的技術知識與網路安全、道德、風險管治和法規遵循的專業知識相結合。這種技能的交叉融合非常罕見，造成了人才瓶頸。世界經濟論壇指出，對人工智慧相關工作的需求正在快速成長，但人工智慧管治專家的人才庫卻未能跟上腳步。此外，設計、實施和維護人工智慧風險風險管理 (AI MRM) 系統的專業知識不足，阻礙了組織成功實施治理框架的能力。這種短缺可能導致管治、監控不平衡，以及對不考慮人工智慧特定風險的通用風險管理技術的依賴。

建構人工智慧專業管治平台

將管治、風險評估和合規性報告功能與 AI 模型生命週期管理相結合的專用平台市場正在不斷成長。與傳統的 GRC 軟體相比，AI MRM 平台解決了 AI 特有的問題，例如可解釋性、偏差檢測、對抗性攻擊預防以及持續學習模型的追蹤。根據雲端安全聯盟 (CSA) 的說法，資料表、模型卡和風險登記冊應該已經成為企業工作流程的一部分。此外，大規模部署 AI 的企業可能會發現，將這些功能整合到整合儀表板中的新興企業和成熟的 GRC 提供者是關鍵的基礎設施。

依賴自動化 MRM 工具的危險

隨著人工智慧支援的 MRM 軟體的不斷發展，企業可能會認為自動化合規儀錶板將完全取代人工監管。人工智慧夥伴關係關係組織和歐盟委員會強調，相關人員的參與、倫理考量和背景風險評估仍需要人工判斷。如果自動化 MRM 工具遺漏了重要的風險，過度依賴它們可能會提供虛假的安全性和合規性保證，使組織面臨業務失敗和監管處罰的風險。

COVID-19的影響：

新冠疫情對人工智慧模型風險管理 (AI MRM) 市場產生了雙重影響。應對疫情相關問題的組織（例如供應鏈最佳化、醫療診斷、救援工作中的欺詐檢測以及遠端客戶支援）迅速採用人工智慧，這往往超過了全面測試和管治的速度，從而增加了偏見、錯誤和模型漂移的風險。人工智慧使用的激增凸顯了對強大的 MRM 框架的需求，以確保緊急情況下的可靠性。此外，經合組織 (OECD) 和美國國家標準與技術研究院 (NIST) 等監管機構和行業協會已開始強調彈性、透明度和持續監控是負責任的人工智慧的關鍵要素，這進一步推動了後疫情時代對人工智慧 MRM 解決方案的需求。

預計預測期內模型風險部分最大

預計模型風險部分將在預測期內佔據最大的市場佔有率。這種主導地位歸因於 AI MRM 框架主要旨在解決特定於模型的風險，例如偏差、過度擬合、缺乏可解釋性、數據品質問題以及性能隨時間推移而下降。模型風險管理在銀行、保險和醫療保健等領域至關重要，這些領域的 AI 模型直接影響關鍵決策，例如信貸核准、詐欺偵測和診斷建議。此外，模型檢驗、針對邊緣情況的測試、記錄假設以及定期監控產出都在 NIST AI 風險管理框架和巴塞爾委員會的模型風險管治原則等監管框架中受到高度重視。

預計詐欺偵測和風險緩解部門在預測期內將以最高複合年成長率成長

預計詐欺偵測和風險緩解領域將在預測期內實現最高成長率。該領域的快速成長源於詐欺活動日益複雜化，尤其是在銀行、金融科技、保險和電子商務領域，這需要能夠即時識別異常的先進人工智慧系統。隨著詐騙手段的演變，企業正在使用具有持續學習能力的人工智慧模型來發現細微的模式，並防止財務和聲譽損失。此外，這些模型必須在嚴格的風險管治下運行，以保持客觀性和可解釋性，並遵守美國《銀行保密法》、歐盟《人工智慧法》和《洗錢防制》等法律。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率，這得益於該地區強大的監管框架、早期採用人工智慧技術以及關鍵技術公司、金融機構和人工智慧管治解決方案提供商的存在。美國聯邦在這​​方面處於世界領先地位，這得益於監督(FRB)、美國監理署 (OCC) 和國家標準與技術研究院 (NIST) 等組織的嚴格合規要求，這些要求要求強力的模型檢驗、監控和管治實踐。此外，人工智慧在銀行、醫療保健和政府服務的快速整合推動了對全面風險管理框架的需求。加拿大的人工智慧道德和透明度措施進一步推動了市場擴張。

複合年成長率最高的地區：

預計亞太地區將在預測期內實現最高的複合年成長率，這得益於數位轉型步伐的加快，政府、銀行、製造業和醫療保健領域對人工智慧的日益普及，以及監管機構對負責任人工智慧的日益重視。除了對人工智慧基礎設施的大量投資外，中國、印度、新加坡和日本等國家也在採用框架和指南來解決模型管治、演算法偏差和資料隱私問題。此外，政府支持的人工智慧舉措，例如新加坡的《人工智慧管治框架》和印度的國家人工智慧策略，正在為長期市場擴張奠定堅實基礎，使亞太地區成為該領域成長最快的地區。

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目錄

第1章執行摘要

第 2 章 簡介

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 主要研究資料
    • 二手研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 介紹
  • 驅動程式
  • 抑制因素
  • 市場機會
  • 威脅
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧模型風險管理市場（按產品提供）

  • 軟體
    • 模型管理
    • 偏差檢測
    • 可解釋的人工智慧工具
  • 服務
    • 專業服務
    • 託管服務

6. 全球人工智慧模型風險管理市場（按部署方法）

  • 本地
  • 雲端基礎
  • 混合

7. 全球人工智慧模型風險管理市場（按風險類型）

  • 模型風險
  • 操作風險
  • 合規風險
  • 聲譽風險
  • 策略風險
  • 道德風險

8. 全球人工智慧模型風險管理市場（按應用）

  • 詐欺偵測和風險降低
  • 資料分類和標記
  • 情緒分析
  • 模型庫存管理
  • 客戶分類與定位
  • 監理合規監控
  • 其他用途

9. 全球人工智慧模型風險管理市場（按最終用戶）

  • 銀行、金融服務和保險（BFSI）
  • 零售與電子商務
  • 資訊科技/通訊
  • 製造業
  • 醫學與生命科​​學
  • 媒體與娛樂
  • 政府/公共部門
  • 其他最終用戶

第 10 章：全球人工智慧模型風險管理市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併與收購（M&A）
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章：企業概況

  • 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.