Summary

The Global AI Model Risk Management Market is valued at approximately USD 5.7 billion in 2024 and is expected to exhibit a compelling CAGR of 12.80% during the forecast period from 2025 to 2035. As enterprises rapidly integrate artificial intelligence into mission-critical workflows, the demand for structured, reliable, and accountable risk governance frameworks has risen sharply. AI Model Risk Management (MRM) has emerged as an indispensable discipline that not only identifies and mitigates model inaccuracies and biases but also helps institutions comply with evolving regulatory mandates and maintain public trust. With machine learning models now influencing key decisions in credit scoring, fraud detection, trading algorithms, and customer analytics, even a minor model failure can cascade into costly reputational and financial damages—making robust risk oversight non-negotiable.
The proliferation of AI models, particularly within banking, insurance, and fintech verticals, has catalyzed the market’s momentum. Institutions are increasingly deploying MRM platforms that provide end-to-end lifecycle oversight—covering model validation, version control, bias detection, interpretability, and audit trails. Vendors are offering sophisticated software solutions with embedded explainable AI (XAI), sandbox testing environments, and real-time anomaly detection to facilitate regulatory alignment with frameworks such as SR 11-7, GDPR, and Basel guidelines. Additionally, growing awareness among C-suite leaders about AI ethics and algorithmic accountability is unlocking budgetary allocation for risk governance initiatives. However, interoperability challenges with legacy systems and a global shortage of skilled AI risk professionals are tempering short-term scalability in certain regions.
Regionally, North America dominates the market due to its technologically advanced financial ecosystem and stringent regulatory culture, spearheaded by agencies like the Federal Reserve and OCC that emphasize model governance. The U.S., in particular, has witnessed a surge in demand for centralized model risk platforms across Tier 1 banks and fintech giants. Meanwhile, Europe is gaining traction, driven by its proactive data protection laws and initiatives such as the EU AI Act which impose transparency and explainability standards for AI systems. Asia Pacific is forecasted to be the fastest-growing market, led by digital banking adoption in countries like India, Singapore, China, and South Korea. Government-backed AI innovation strategies, along with a growing presence of global consultancies offering MRM services, are expected to accelerate adoption across the region.
Major market player included in this report are:
• IBM Corporation
• SAS Institute Inc.
• FICO
• Moody’s Analytics
• Google LLC
• Microsoft Corporation
• Oracle Corporation
• Amazon Web Services, Inc.
• DataRobot
• Altair Engineering Inc.
• H2O.ai
• Zest AI
• KPMG
• Accenture PLC
• Infosys Limited
Global AI Model Risk Management Market Report Scope:
• Historical Data – 2023, 2024
• Base Year for Estimation – 2024
• Forecast period – 2025–2035
• Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
• Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
• Customization Scope – Free report customization (equivalent up to 8 analysts’ working hours) with purchase. Addition or alteration to country, regional & segment scope*
The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values for the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within the countries involved in the study. The report also provides detailed information about crucial aspects, such as driving factors and challenges, which will define the future growth of the market. Additionally, it incorporates potential opportunities in micro-markets for stakeholders to invest, along with a detailed analysis of the competitive landscape and product offerings of key players. The detailed segments and sub-segments of the market are explained below:
By Offering:
• Software
 • By Type
 • By Deployment Mode
• Services
By Risk Type:
• Model Risk
• Compliance Risk
• Operational Risk
• Cyber Risk
• Others
By Application:
• Risk Management
• Fraud Detection
• Credit Assessment
• Regulatory Compliance
• Others
By Vertical:
• Banking
• Financial Services
• Insurance
• Healthcare
• Retail
• Government
• Others
By Region:
North America
• U.S.
• Canada
Europe
• UK
• Germany
• France
• Spain
• Italy
• Rest of Europe
Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• Rest of Asia Pacific
Latin America
• Brazil
• Mexico
Middle East & Africa
• UAE
• Saudi Arabia
• South Africa
• Rest of Middle East & Africa
Key Takeaways:
• Market Estimates & Forecast for 10 years from 2025 to 2035.
• Annualized revenues and regional level analysis for each market segment.
• Detailed analysis of geographical landscape with Country level analysis of major regions.
• Competitive landscape with information on major players in the market.
• Analysis of key business strategies and recommendations on future market approach.
• Analysis of competitive structure of the market.
• Demand side and supply side analysis of the market.

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Table of Contents

Table of Contents
Chapter 1. Global AI Model Risk Management Market Report Scope & Methodology
1.1. Research Objective
1.2. Research Methodology
1.2.1. Forecast Model
1.2.2. Desk Research
1.2.3. Top-Down and Bottom-Up Approach
1.3. Research Attributes
1.4. Scope of the Study
1.4.1. Market Definition
1.4.2. Market Segmentation
1.5. Research Assumption
1.5.1. Inclusion & Exclusion
1.5.2. Limitations
1.5.3. Years Considered for the Study
Chapter 2. Executive Summary
2.1. CEO/CXO Standpoint
2.2. Strategic Insights
2.3. ESG Analysis
2.4. Key Findings
Chapter 3. Global AI Model Risk Management Market Forces Analysis
3.1. Market Forces Shaping the Global AI Model Risk Management Market (2024–2035)
3.2. Drivers
3.2.1. Rapid Integration of AI into Mission-Critical Workflows (Key Driver)
3.2.2. Evolving Regulatory Mandates (GDPR, SR 11-7, Basel) (Key Driver)
3.2.3. Rising C-Suite Awareness of AI Ethics and Accountability (Key Driver)
3.3. Restraints
3.3.1. Interoperability Challenges with Legacy Systems (Key Restraint)
3.3.2. Global Shortage of Skilled AI Risk Professionals (Key Restraint)
3.4. Opportunities
3.4.1. Rapid Digital Banking Adoption in Asia Pacific (Key Opportunity)
3.4.2. Government-Backed AI Innovation Strategies (Key Opportunity)
Chapter 4. Global AI Model Risk Management Industry Analysis
4.1. Porter’s Five Forces Model
4.1.1. Bargaining Power of Buyers
4.1.2. Bargaining Power of Suppliers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s Five Forces Forecast Model (2024–2035)
4.3. PESTEL Analysis
4.3.1. Political
4.3.2. Economic
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top Investment Opportunities
4.5. Top Winning Strategies (2025)
4.6. Market Share Analysis (2024–2025)
4.7. Global Pricing Analysis and Trends 2025
4.8. Analyst Recommendation & Conclusion
Chapter 5. Global AI Model Risk Management Market Size & Forecasts by Offering 2025–2035
5.1. Market Overview
5.2. Software
5.2.1. By Type
5.2.2. By Deployment Mode
5.3. Services
Chapter 6. Global AI Model Risk Management Market Size & Forecasts by Risk Type 2025–2035
6.1. Market Overview
6.2. Model Risk
6.3. Compliance Risk
6.4. Operational Risk
6.5. Cyber Risk
6.6. Others
Chapter 7. Global AI Model Risk Management Market Size & Forecasts by Application 2025–2035
7.1. Market Overview
7.2. Risk Management
7.3. Fraud Detection
7.4. Credit Assessment
7.5. Regulatory Compliance
7.6. Others
Chapter 8. Global AI Model Risk Management Market Size & Forecasts by Vertical 2025–2035
8.1. Market Overview
8.2. Banking
8.3. Financial Services
8.4. Insurance
8.5. Healthcare
8.6. Retail
8.7. Government
8.8. Others
Chapter 9. Global AI Model Risk Management Market Size & Forecasts by Region 2025–2035
9.1. Regional Market Snapshot
9.2. Top Leading & Emerging Countries
9.3. North America
9.3.1. U.S.
9.3.1.1. Offering breakdown size & forecasts, 2025–2035
9.3.1.2. Application breakdown size & forecasts, 2025–2035
9.3.2. Canada
9.3.2.1. Offering breakdown size & forecasts, 2025–2035
9.3.2.2. Application breakdown size & forecasts, 2025–2035
9.4. Europe
9.4.1. UK
9.4.1.1. Offering breakdown size & forecasts, 2025–2035
9.4.1.2. Application breakdown size & forecasts, 2025–2035
9.4.2. Germany
9.4.3. France
9.4.4. Spain
9.4.5. Italy
9.4.6. Rest of Europe
9.5. Asia Pacific
9.5.1. China
9.5.2. India
9.5.3. Japan
9.5.4. Australia
9.5.5. South Korea
9.5.6. Rest of Asia Pacific
9.6. Latin America
9.6.1. Brazil
9.6.2. Mexico
9.7. Middle East & Africa
9.7.1. UAE
9.7.2. Saudi Arabia
9.7.3. South Africa
9.7.4. Rest of Middle East & Africa
Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. IBM Corporation
10.2.1. Company Overview
10.2.2. Key Executives
10.2.3. Company Snapshot
10.2.4. Financial Performance (Subject to Data Availability)
10.2.5. Product/Services Portfolio
10.2.6. Recent Developments
10.2.7. Market Strategies
10.2.8. SWOT Analysis
10.3. SAS Institute Inc.
10.4. FICO
10.5. Moody’s Analytics
10.6. Google LLC
10.7. Microsoft Corporation
10.8. Oracle Corporation
10.9. Amazon Web Services, Inc.
10.10. DataRobot
10.11. Altair Engineering Inc.
10.12. H2O.ai
10.13. Zest AI
10.14. KPMG
10.15. Accenture PLC
10.16. Infosys Limited