NEW DELHI, India, Nov. 7, 2025 /PRNewswire/ — According to recent analysis by Markntel Advisors, the Global AI Model Risk Management (MRM) Market is experiencing accelerated growth, driven by soaring enterprise adoption of artificial intelligence (AI), heightened regulatory scrutiny, and the rising need for transparency and explainability in machine learning (ML) models across industries. As AI systems increasingly influence critical business decisions, from credit lending and fraud detection to healthcare diagnostics and insurance underwriting, the emphasis on robust model validation, governance, and compliance frameworks is becoming imperative.
North America dominates the global market with 38% of market share, led by the United States, where regulatory agencies such as the Federal Reserve, OCC, and FDIC continue to enhance guidelines for model risk governance (e.g., SR 11-7 framework). The region’s leadership is also supported by the widespread use of AI models in banking, insurance, and defense applications, coupled with the presence of technology-driven MRM vendors offering integrated platforms for model inventory, monitoring, and compliance automation. In addition to this, Europe closely follows, propelled by stringent regulatory frameworks like the EU’s AI Act and GDPR compliance requirements demanding transparency and accountability in data-driven operations.
The Global AI Model Risk Management Market was valued at around USD 6.41 billion in 2025 and is projected to reach USD 14.55 billion by 2032, growing at a CAGR of 12.42% during 2026–32. Global companies in AI Model Risk Management industry such as SAS Institute, IBM, FIS, and ModelOp are actively expanding their product portfolios through AI governance capabilities, cloud-native integrations, and advanced analytics to strengthen enterprise trust in AI-driven decision systems.
In May 2024, Amazon Web Services expanded its strategic partnership with CrowdStrike, a cybersecurity solutions provider, to modernize cybersecurity integration and facilitate cloud transformation. Amazon Web Services consolidated its cybersecurity defenses under the CrowdStrike Falcon platform, ensuring comprehensive protection from individual code segments to entire cloud infrastructures and from devices to data. This consolidation reflects the growing need for secure, automated risk management frameworks in cloud environments, thereby supporting AI model risk management market growth.
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Similarly, in May 2024, International Business Machines Corporation partnered with Palo Alto Networks to provide customers with AI-driven security results. The partnership aims to simplify security operations, effectively halt threats on a large scale, and expedite incident resolution through a comprehensive AI-enabled strategy. This alliance enhances AI model risk management by embedding advanced cybersecurity, a crucial factor as AI models increasingly operate in high-stakes environments where compliance and risk mitigation are critical.
Overall, these strategic partnerships illustrate how the convergence of AI, cybersecurity, and cloud technologies is driving the adoption of AI Model Risk Management solutions. They underscore a market trend favoring robust AI governance frameworks that ensure model transparency, regulatory compliance, and operational resilience in an increasingly digital and regulated landscape.
AI Model Risk Management Industry Key Takeaways:
- Historical Years: 2021–24
- Base Year: 2025
- Forecast Years: 2026–32
- Market Value in 2025: USD 6.41 Billion
- Market Value by 2032: USD 14.55 Billion
- CAGR (2026–32): 12.42%
- Prominent Region: North America
- Leading Offering: Software Segment
- Dominating Application: Fraud Detection and Risk Reduction
Key Growth Catalysts Strengthening AI MRM Market Expansion
Mandated Model Governance Frameworks in Financial Services and Beyond
The evolving regulatory landscape across the globe is compelling banks, financial institutions, and increasingly other industries to adopt rigorous AI model risk governance frameworks. In North America, the Federal Reserve’s Supervisory Guidance on Model Risk Management (SR 11-7) remains a key reference that mandates comprehensive validation, performance monitoring, and bias detection for critical models to protect financial system stability. In Europe, the EU AI Act sets out a risk-based regulatory framework requiring transparency, accountability, and robustness in AI systems, emphasizing the governance of high-risk AI models used in sectors like finance, healthcare, and transportation.
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Beyond these, several other global frameworks influence AI Model Risk Management:
• The US Office of the Comptroller of the Currency (OCC) issues guidance reinforcing supervisory expectations on AI risk governance frameworks, stressing model lifecycle management and documentation.
• The Monetary Authority of Singapore (MAS) provides detailed principles for responsible AI deployment, highlighting risk management, transparency, and ethical AI use.
• In Australia, the Australian Prudential Regulation Authority (APRA) emphasizes robust governance and validation for AI and machine learning models used in financial reporting and risk assessment.
These regulatory requirements collectively establish a non-negotiable baseline that mandates detailed model documentation, bias assessment, tracking of model lineage and versioning, continuous performance monitoring, and ethical risk evaluation. The growing global ecosystem of AI governance frameworks creates sustained demand for specialized AI Model Risk Management systems capable of meeting diverse, region-specific regulatory expectations while ensuring operational resilience and ethical compliance.
Proliferation of Generative AI and Foundation Models Creating New Risk Profiles
The rise of generative AI models like GPT-4, Claude, and Gemini introduces unique compliance challenges for enterprises. A key risk is AI hallucinations, where models produce false or misleading outputs, posing severe threats in sensitive sectors like finance. Such inaccuracies can lead to noncompliance, financial losses, reputational damage, and regulatory penalties. Managing data provenance is also critical, as generative AI often processes sensitive information, raising privacy concerns under laws like GDPR. Additionally, mitigating bias embedded in training data demands continuous ethical auditing. These risks compel organizations to use advanced model risk management tools capable of real-time drift detection, ethical compliance scoring, and ensuring transparency and explainability to maintain trust and regulatory adherence in deploying generative AI systems.
What Significant Factor is projected to Open New Doors for AI Model Risk Management Market Growth?
Demand for Transparent and Explainable AI Driving Advanced MRM Feature Development
Rising regulatory mandates and consumer scrutiny around AI transparency are driving significant demand for Explainable AI (XAI) features within AI Model Risk Management (MRM) platforms. Organizations face increasing pressure to provide clear, human-understandable explanations of AI decisions, particularly in regulated sectors like finance, healthcare, and public administration. Explainability enables firms to justify AI-driven outcomes, ensure compliance with strict frameworks such as the EU AI Act, and fulfill audit and oversight requirements. This transparency is critical to identifying biases, validating model behavior, and building stakeholder trust. As a result, MRM tools incorporating bias detection, ethical audits, and decision visualizations open robust opportunities for enhancing regulatory readiness, strengthening governance frameworks, and fostering responsible AI adoption going forward.
Key Challenges Restring the growth of AI Model Risk Management Market?
Talent and Skill Shortages
The scarcity of specialized AI governance professionals and model risk officers is a significant barrier limiting enterprise adoption of AI Model Risk Management (MRM) systems, especially in emerging economies. These regions often lack sufficient access to trained personnel who understand the complex interplay of AI technologies, regulatory requirements, and ethical considerations essential for effective model risk oversight. The rapid evolution of AI and regulatory frameworks intensifies the need for continuous upskilling, but educational programs and professional training remain underdeveloped or inaccessible in many areas. Additionally, brain drain, where skilled professionals migrate to developed markets, exacerbates the shortage, leaving local organizations understaffed. This talent gap restricts MRM implementation and compliance capabilities, slowing digital transformation initiatives. Addressing this challenge requires targeted investments in AI literacy, vocational training, government-industry collaboration, and fostering local innovation ecosystems to build and retain capable AI risk management workforces.
Key Trends Transforming Global AI Model Risk Management Market Outlook in 2026
Key Trends in AI Model Risk Management Market:
• Shift Toward AI Governance-as-a-Service (AI-GaaS): Increasing adoption of cloud-based, subscription models for AI risk management that provide scalable, on-demand governance capabilities without heavy upfront investments.
• Integration of Continuous Learning and Adaptive Models: Emergence of MRM platforms supporting dynamic model retraining and monitoring to address rapid changes in data distributions and maintain accuracy over time.
• Rise of Industry-Specific AI Risk Frameworks: Development of tailored risk management approaches specific to sectors such as healthcare, manufacturing, and retail, reflecting unique regulatory and operational requirements.
• Growing Focus on Data Quality and Provenance Management: Enhanced tools for ensuring the integrity, lineage, and auditability of input data to models, recognizing data as a critical source of downstream risk.
• Increased Use of Automation and AI in Risk Detection: Deployment of AI-driven analytics and anomaly detection within MRM systems to proactively identify emerging risks and compliance breaches with minimal manual intervention.
• Emphasis on Cross-Enterprise Collaboration: Platforms evolving to support multi-stakeholder workflows involving regulators, model developers, business owners, and auditors, facilitating transparency and coordinated risk mitigation.
These trends showcase how the market is evolving through service models, automation, domain specialization, and collaborative governance, offering new avenues for innovation and growth beyond regulatory and technological drivers.
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Major Companies Leading the Global AI Model Risk Management Market
- Alteryx,
- Amazon Web Services,
- Accenture,
- DataBricks,
- Google,
- Delteck,
- International Business Machines Corporation,
- Congruity360,
- Wolters Kluwer N.V.,
- LogicManager,
- LogicGate,
- Oracle,
- Microsoft,
- ModelOp,
- SAS Institute,
- UpGuard, and others
Global AI Model Risk Management Market (2026–32)
• By Risk Type: Security Risk, Ethical Risk, Operational Risk
• By Offering: Software, Services
• By Application: Fraud Detection and Risk Reduction, Data Classification and Labelling, Sentiment Analysis, Model Inventory Management, Customer Segmentation and Targeting, Regulatory Compliance Monitoring, Others
• By End User: BFSI, Retail & E-commerce, IT & Telecom, Manufacturing, Healthcare & Life Sciences, Media & Entertainment, Government and Public Sector
AI Model Risk Management Industry Regional Projection
• Asia-Pacific: China, India, Japan, South Korea, Thailand, Indonesia, Philippines, Malaysia, Australia, Vietnam
• Europe: Germany, France, The UK, Italy, Spain, Rest of Europe
• North America: The US, Canada, Mexico
• South America: Brazil, Argentina, Rest of South America
• Middle East and Africa: The UAE, Saudi Arabia, South Africa, Rest of the Middle East & Africa
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About Us
MarkNtel Advisors is a globally recognized industry research firm delivering in-depth market intelligence across advanced technology sectors, including the AI Model Risk Management market. Our syndicated reports offer comprehensive insights into market dynamics, regulatory frameworks, risk mitigation trends, and competitive strategies shaping the AI governance ecosystem worldwide.
Leveraging advanced analytics, quantitative modeling, and validated technological and policy data, we empower technology providers, financial institutions, healthcare organizations, and policymakers to navigate evolving AI compliance landscapes and capitalize on emerging growth opportunities. Our expertise spans global and regional markets such as North America, Europe, and Asia-Pacific, emphasizing regulatory developments, innovation in AI risk mitigation solutions, and the integration of AI with cybersecurity standards. Through specialized consulting, we enable clients to achieve strategic advantage and foster resilient, ethical AI adoption amid rising governance demands and operational complexities.
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