2026 Global: Artificial Intelligence (Ai) Model Risk Management Market-Competitive Review (2032) report
Description
The 2026 Global: Artificial Intelligence (Ai) Model Risk Management Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) model risk management market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) model risk management market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Microsoft, IBM, Amazon Web Services (AWS), Google, SAS Institute, Deloitte, Accenture, DataRobot, Fiddler AI, and Credo AI are widely recognized as major companies shaping the Artificial Intelligence Model Risk Management market. Microsoft leverages Azure’s governance and compliance tooling alongside Microsoft Purview and Azure ML to provide enterprise-grade model lifecycle controls, explainability, and integrated monitoring for model risk management across cloud-native and hybrid deployments. IBM offers Watson OpenScale, Watson Studio, and governance frameworks that deliver continuous model monitoring, fairness and bias detection, explainability, and audit trails tailored for regulated industries, supported by IBM’s long-standing risk consulting and security practice. AWS supplies a broad stack—centered on SageMaker and its model-monitoring, explainability, and bias-detection features—combined with controls in IAM, logging, and data encryption that enable scalable MRM (Model Risk Management) for organizations using cloud-native ML services. Google (and Google Cloud) provides model-governance capabilities through Cloud AI Platform, Vertex AI, and integrated tools for explainability, bias detection, and provenance tracking that support enterprises seeking managed MRM features tied to TensorFlow and other ML frameworks.
SAS Institute brings a mature, audit-focused approach to model risk management with SAS Model Risk Management and risk-modeling toolsets that emphasize reproducibility, stress testing, scenario analysis, and regulatory alignment for finance and insurance sectors. Deloitte and Accenture act as leading professional services integrators that combine proprietary toolchains, third-party MRM platforms, and advisory services to define governance policies, validation processes, and enterprise risk frameworks for AI deployments across highly regulated organizations. DataRobot offers an automated ML platform with built-in model governance, lineage, monitoring, and explainability modules intended to accelerate validated productionization of models while maintaining comprehensive documentation and auditability for model validators and compliance teams. Fiddler AI provides production-oriented model monitoring with real-time drift detection, bias measurement, and explainability features focused on operationalizing MRM for live ML systems and supporting incident investigation and remediation workflows. Credo AI supplies policy-driven compliance and assessment tooling that codifies ethical and regulatory requirements into structured assessments, produces standardized documentation and evidence packages for audits, and integrates with monitoring tools to operationalize governance across the model lifecycle.
Collectively these ten firms span cloud hyperscalers, legacy analytics vendors, specialized MRM platforms, and systems integrators, covering the principal needs of AI model risk management: model inventory and lineage, continuous performance and fairness monitoring, explainability, documentation and audit trails, and enterprise governance and policy enforcement. Hyperscalers (Microsoft, AWS, Google) emphasize scalable, integrated controls within cloud ecosystems. Established analytics vendors (IBM, SAS, DataRobot) bring audit-ready tooling and domain depth for regulated sectors. Professional services (Deloitte, Accenture) focus on governance frameworks and deployment risk reduction at organizational scale. Niche platform providers (Fiddler AI, Credo AI) concentrate on monitoring, explainability, and compliance automation that complement larger stacks or serve specialist use cases.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) model risk management market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) model risk management market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
Microsoft, IBM, Amazon Web Services (AWS), Google, SAS Institute, Deloitte, Accenture, DataRobot, Fiddler AI, and Credo AI are widely recognized as major companies shaping the Artificial Intelligence Model Risk Management market. Microsoft leverages Azure’s governance and compliance tooling alongside Microsoft Purview and Azure ML to provide enterprise-grade model lifecycle controls, explainability, and integrated monitoring for model risk management across cloud-native and hybrid deployments. IBM offers Watson OpenScale, Watson Studio, and governance frameworks that deliver continuous model monitoring, fairness and bias detection, explainability, and audit trails tailored for regulated industries, supported by IBM’s long-standing risk consulting and security practice. AWS supplies a broad stack—centered on SageMaker and its model-monitoring, explainability, and bias-detection features—combined with controls in IAM, logging, and data encryption that enable scalable MRM (Model Risk Management) for organizations using cloud-native ML services. Google (and Google Cloud) provides model-governance capabilities through Cloud AI Platform, Vertex AI, and integrated tools for explainability, bias detection, and provenance tracking that support enterprises seeking managed MRM features tied to TensorFlow and other ML frameworks.
SAS Institute brings a mature, audit-focused approach to model risk management with SAS Model Risk Management and risk-modeling toolsets that emphasize reproducibility, stress testing, scenario analysis, and regulatory alignment for finance and insurance sectors. Deloitte and Accenture act as leading professional services integrators that combine proprietary toolchains, third-party MRM platforms, and advisory services to define governance policies, validation processes, and enterprise risk frameworks for AI deployments across highly regulated organizations. DataRobot offers an automated ML platform with built-in model governance, lineage, monitoring, and explainability modules intended to accelerate validated productionization of models while maintaining comprehensive documentation and auditability for model validators and compliance teams. Fiddler AI provides production-oriented model monitoring with real-time drift detection, bias measurement, and explainability features focused on operationalizing MRM for live ML systems and supporting incident investigation and remediation workflows. Credo AI supplies policy-driven compliance and assessment tooling that codifies ethical and regulatory requirements into structured assessments, produces standardized documentation and evidence packages for audits, and integrates with monitoring tools to operationalize governance across the model lifecycle.
Collectively these ten firms span cloud hyperscalers, legacy analytics vendors, specialized MRM platforms, and systems integrators, covering the principal needs of AI model risk management: model inventory and lineage, continuous performance and fairness monitoring, explainability, documentation and audit trails, and enterprise governance and policy enforcement. Hyperscalers (Microsoft, AWS, Google) emphasize scalable, integrated controls within cloud ecosystems. Established analytics vendors (IBM, SAS, DataRobot) bring audit-ready tooling and domain depth for regulated sectors. Professional services (Deloitte, Accenture) focus on governance frameworks and deployment risk reduction at organizational scale. Niche platform providers (Fiddler AI, Credo AI) concentrate on monitoring, explainability, and compliance automation that complement larger stacks or serve specialist use cases.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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