Dublin, March 17, 2025 (GLOBE NEWSWIRE) -- The "Artificial Intelligence Model Risk Management - Global Strategic Business Report" report has been added to ResearchAndMarkets.com's offering.

The global market for Artificial Intelligence Model Risk Management was valued at US$6.7 Billion in 2024 and is projected to reach US$13.6 Billion by 2030, growing at a CAGR of 12.6% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions.

Artificial Intelligence (AI) is revolutionizing model risk management (MRM) by providing financial institutions, insurance companies, and other businesses with advanced tools to assess, monitor, and mitigate risks associated with AI-driven models. In traditional risk management, manual processes and simplistic models were used to evaluate potential risks in financial, operational, and strategic decision-making. However, as AI models have become more complex and embedded in critical business functions, the need for sophisticated and automated risk management solutions has grown substantially.

What's Driving the Growth of the AI in Model Risk Management Market?

The growth in the Artificial Intelligence in Model Risk Management market is driven by a combination of technological advancements, regulatory pressures, and the increasing complexity of AI-driven business operations. As organizations continue to integrate AI into core processes such as decision-making, fraud detection, and financial forecasting, the need to manage the associated risks grows. AI-driven MRM solutions are providing the tools needed to monitor, assess, and mitigate these risks, thus ensuring that organizations can confidently deploy AI models across their operations.

Regulatory and ethical considerations are also central to the market's growth. The increasing focus on ensuring AI fairness, transparency, and accountability has pushed companies to adopt advanced MRM solutions that can provide greater visibility into how AI models operate and make decisions. Government regulations, particularly in sectors like banking, healthcare, and insurance, are enforcing stricter standards, requiring organizations to demonstrate that their models are free of biases and inaccuracies. AI solutions that streamline compliance and provide robust auditing capabilities are, therefore, crucial to organizations seeking to meet these standards.

The growing focus on data privacy and the ethical implications of AI also plays a significant role in the market's expansion. As AI systems become more pervasive, managing the ethical and legal implications of AI decision-making is paramount. AI-powered model risk management systems allow organizations to identify and mitigate risks associated with data misuse, algorithmic bias, and lack of transparency, ensuring they operate within ethical boundaries and maintain consumer trust. These factors, combined with the ongoing evolution of AI technologies, are driving the rapid adoption and growth of AI in model risk management.

What Drives the Adoption of AI in Model Risk Management?

The increasing reliance on AI models in sectors like banking, insurance, and investment is a major driver of AI adoption in model risk management. As financial institutions and corporations increasingly adopt AI for credit scoring, fraud detection, algorithmic trading, and underwriting, the complexity and scale of their models have expanded. This makes traditional risk management techniques inadequate for ensuring the reliability and fairness of these models. AI solutions are now being used to address this gap, providing businesses with real-time risk assessments and automated model validation tools.

Regulatory pressures are another key driver. As AI models become more widely used in decision-making processes, governments and regulatory bodies are imposing stricter guidelines and requirements for model transparency, fairness, and accountability. In financial sectors, for example, there is increasing demand for transparency in AI-based credit scoring and lending decisions to prevent discrimination and bias. AI-driven model risk management solutions help companies comply with these regulations by providing detailed reports and assessments of model performance, bias, and fairness. This ensures that models are operating within acceptable limits and can withstand regulatory scrutiny.

Additionally, organizations are increasingly prioritizing data integrity and the ethical use of AI. As AI models become more ingrained in decision-making, they also become more susceptible to biases that could impact outcomes. AI model risk management addresses these concerns by identifying potential sources of bias, ensuring that models are fair and equitable. This proactive approach to ethical risk mitigation is helping build trust with customers, regulators, and stakeholders, further driving the adoption of AI in model risk management.

Key Insights:

• Market Growth: Understand the significant growth trajectory of the AI Model Risk Management Software segment, which is expected to reach US$7.9 Billion by 2030 with a CAGR of a 11%. The AI Model Risk Management Services segment is also set to grow at 15.1% CAGR over the analysis period.
• Regional Analysis: Gain insights into the U.S. market, valued at $1.8 Billion in 2024, and China, forecasted to grow at an impressive 11.9% CAGR to reach $2.1 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific.

Report Features:

• Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030.
• In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
• Company Profiles: Coverage of players such as Accenture Plc, C3.ai, Inc., DataRobot, Inc., Deloitte Touche Tohmatsu Ltd., H2O.ai and more.
• Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.

Segments

• Component (Software Component, Services Component)
• Risk Type (Operational Risk, Security Risk, Ethical Risk, Other Risks)
• Application (Fraud Detection & Risk Reduction Application, Data Classification & Labelling Application, Regulatory Compliance Monitoring Application, Sentiment Analysis Application, Other Applications)
• Vertical (IT & Telecom Vertical, BFSI Vertical, Healthcare & Life Sciences Vertical, Government & Public Sector Vertical, Retail & E-Commerce Vertical, Other Verticals)

Key Attributes:

Key Topics Covered:

MARKET OVERVIEW

• Influencer Market Insights
• World Market Trajectories
• Economic Frontiers: Trends, Trials & Transformations
• Artificial Intelligence Model Risk Management - Global Key Competitors Percentage Market Share in 2025 (E)
• Competitive Market Presence - Strong/Active/Niche/Trivial for Players Worldwide in 2025 (E)

MARKET TRENDS & DRIVERS

• Regulatory Scrutiny on AI Models Strengthens Demand for Risk Management Frameworks
• AI-Driven Model Validation Tools Propel Growth in Financial Risk Management
• Integration of Explainable AI (XAI) Sets the Stage for Enhanced Transparency
• AI-Powered Bias Detection Tools Highlight Opportunities in Ethical Model Development
• Growing Complexity of AI Models Spurs Demand for Comprehensive Risk Assessment Solutions
• Advancements in AI for Predictive Risk Analytics Propel Growth in Banking and Finance
• AI-Powered Scenario Analysis Bodes Well for Stress Testing Across Industries
• AI in Fraud Risk Management Expands Market for Real-Time Detection Solutions
• AI in Climate Risk Assessment Sets the Stage for Growth in ESG Compliance
• AI for Supply Chain Risk Management Expands Use Cases in Global Logistics
• Growing Push for AI Model Accountability Highlights Market for Governance Tools

FOCUS ON SELECT PLAYERS:Some of the 42 companies featured in this Artificial Intelligence Model Risk Management market report include:

• Accenture Plc
• C3.ai, Inc.
• DataRobot, Inc.
• Deloitte Touche Tohmatsu Ltd.
• H2O.ai
• IBM Corporation
• LogicGate, Inc.
• McKinsey & Company
• Microsoft Corporation
• SAS Institute, Inc.

For more information about this report visit https://www.researchandmarkets.com/r/989rsr

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