AI Model Risk Management Market Projected to Reach $19,036.19 Million by 2034, Growing at a CAGR of 12.8%

Market Overview:

The global AI model risk management market size is expected to reach USD 19,036.19 million by 2034, exhibiting a CAGR of 12.8% during the forecast period.

Model risk management involves monitoring the risks that come from the possible negative outcomes of decisions made using inaccurate or misapplied models. The goal of model risk management is to utilize methods and procedures to recognize, quantify, and reduce model risks.

Growth Drivers and Opportunities:

• Incorporating AI into robotics and automation in manufacturing requires strong risk management to ensure the safety, efficiency, and reliability of automated systems, thereby driving market expansion.
• The extensive utilization of AI technologies to improve various aspects of online retail has been brought about by the rapid growth of e-commerce, fueled by rising internet penetration and shifting consumer behavior.

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• Alteryx
• C3 AI
• DataBricks
• H2O.ai
• IBM
• iManage
• LogicGate
• LogicManager
• MathWorks
• Microsoft
• ModelOp
• SAS Institute
• UpGuard

Key Trends in the AI Model Risk Management Market:

• Integration of AI with Governance, Risk, and Compliance (GRC) Platforms: Many organizations are incorporating AI risk management tools into broader GRC frameworks to streamline operations.
• Focus on Bias Detection and Mitigation: Vendors are emphasizing solutions that identify and correct biases in AI models to ensure ethical outcomes.
• Adoption of Explainable AI (XAI): Explainability is becoming a cornerstone of AI risk management, ensuring models are interpretable and justifiable.
• AI in Risk Assessment: AI itself is being used to evaluate risks, providing real-time insights and predictive analytics.
• Customized Solutions for Industry Needs: Sector-specific risk management tools are being developed to address unique challenges in industries like healthcare and finance.

Research Scope of the Market:

The research scope of the AI model risk management market includes:

• Market Size and Forecast: Analyzing the current market size and projecting growth across regions and industries.
• Competitive Landscape: Assessing major players, including software vendors, consulting firms, and niche solution providers.
• Technological Innovations: Evaluating advancements in tools for risk detection, model validation, and compliance monitoring.
• End-User Analysis: Understanding the adoption patterns across sectors such as banking, retail, government, and manufacturing.
• Regulatory Impact: Studying the influence of laws and standards on market growth and solution development.

Market Segmentation:

The AI model risk management market is mainly segmented based on offering, risk type, application, vertical, and region. Based on application analysis, the largest market share was held by the fraud detection and risk reduction segment in 2023. The e-commerce industry, along with financial services and various other industries, has seen a substantial rise in online transactions, resulting in higher vulnerability towards fraudulent behaviors.

Furthermore, according to the vertical analysis, the healthcare and life science segment is anticipated to be the fastest-growing market. The incorporation and implementation of AI in healthcare and life sciences are transforming diagnostics, treatment planning, drug discovery, and patient care with outstanding precision and effectiveness.

AI Model Risk Management Market, Offering Outlook (Revenue – USD Million, 2020-2034)

• Software by Type
  • Model Management
  • Bias Detection
  • Explainable AI Tools
  • Others
• Software by Deployment Mode
  • Cloud
  • On-Premises
• Services
  • Professional Services
  • Consulting and Advisory
  • Managed Services
  • Others

AI Model Risk Management Market, Risk Type Outlook (Revenue – USD Million, 2020-2034)

• Security Risk
• Ethical Risk
• Operational Risk

AI Model Risk Management Market, Application Outlook (Revenue – USD Million, 2020-2034)

• Fraud Detection and Risk Reduction
• Data Classification and Labelling
• Sentiment Analysis
• Model Inventory Management
• Customer Segmentation and Targeting
• Regulatory Compliance Monitoring
• Other Applications

AI Model Risk Management Market, Vertical Outlook (Revenue – USD Million, 2020-2034)

• Banking, Financial Services, And Insurance (BFSI)
• Government and Public Sector
• Healthcare and Life science
• IT & Telecommunication
• Manufacturing
• Media & Entertainment
• Retail & E-Commerce
• Other Verticals

AI Model Risk Management Industry Updates:

• May 2024: Union Bank of India Partners with SAS Solutions
  Union Bank of India has collaborated with SAS Solutions to modernize its risk management frameworks. The partnership focuses on enhancing the bank’s risk procedures and reporting capabilities by leveraging advanced model risk management technologies.
• May 2024: AWS and CrowdStrike Expand Partnership
  Amazon Web Services (AWS) and CrowdStrike have strengthened their collaboration to streamline cybersecurity and accelerate cloud transformation. AWS has adopted CrowdStrike’s Falcon platform, consolidating its cybersecurity tools with Falcon Cloud Security and implementing Falcon Next-Gen SIEM and Identity Threat Detection and Response. Concurrently, CrowdStrike is expanding its use of AWS services to drive innovation in cloud security and AI-powered cybersecurity applications.
• September 2022: C3 AI and Google Cloud Extend Partnership
  C3 AI and Google Cloud extended their collaboration for an additional three years to boost joint sales and co-develop advanced AI-driven solutions. C3 AI’s complete suite of enterprise AI applications is now integrated with Google Cloud, leveraging tools like Vertex AI and data analytics services. This partnership emphasizes scaling go-to-market strategies and developing innovative AI applications tailored to enterprise needs.

The AI model risk management market is rapidly evolving as organizations increasingly adopt artificial intelligence (AI) technologies across various sectors. The market encompasses a range of solutions and practices designed to manage and mitigate the risks associated with the deployment and operation of AI models. These risks include model inaccuracies, biases, compliance issues, cybersecurity threats, and operational failures. Organizations in sectors such as finance and healthcare are leveraging AI to enhance operational efficiency, improve decision-making, and gain competitive advantages.