AI Model Risk Management Market Size - By Component, By Deployment Model, By Risk, By Application, By End Use, Forecast, 2024 - 2032

AI Model Risk Management Market Size

The global AI model risk management market size was valued at USD 5.3 billion in 2023 and is projected to grow at a CAGR of 11.1% between 2024 and 2032. The increasing regulatory compliance requirements worldwide are expected to drive the market growth. As governments and regulatory bodies impose stricter guidelines regarding the use of AI, organizations are compelled to adopt robust risk management frameworks to ensure compliance. AI model risk management solutions help organizations automate monitoring and validation processes, enabling them to demonstrate compliance effectively while minimizing the risks associated with non-compliance.
 

For instance, In July 2024, the National Institute of Standards and Technology (NIST) launched Dioptra, a new open-source software tool for evaluating security risks in AI models. This tool helps businesses and government agencies to assess and verify AI tools. The regulatory landscape propels the demand for advanced AI-driven tools that can analyze model performance and provide actionable insights.
 

The increasing complexity of AI models being deployed is anticipated to propel the AI model risk management market growth. As organizations adopt more sophisticated AI technologies, including deep learning and ensemble methods, the associated risks also escalate. Organizations must ensure that these models are transparent, interpretable, and reliable, which requires comprehensive validation and monitoring. By leveraging advanced analytics and automated monitoring, businesses can better understand model behavior and make informed decisions regarding their deployment.
 

AI Model Risk Management Market Trends

As businesses recognize the value of leveraging data for strategic insights, they are adopting AI models to enhance their decision-making processes. This trend highlights the need for effective risk management to ensure that these models operate reliably and ethically. With more organizations relying on AI for critical decisions, the potential risks associated with model failures or biases become more prominent. By implementing these solutions, companies can enhance their confidence in AI-driven decisions, ensuring accountability and transparency.
 

To meet growing market demand major players are focusing on strategic initiatives. For instance, in June 2024, Yields partnered with Evalueserve to enhance MRM at financial institutions. By integrating Yields’ model risk management platform Evalueserve will benefit from custom solutions that enhance their risk management capabilities, ensure regulatory compliance, and support efficient operational scaling.
 

One significant pitfall restraining market growth is the challenge of data quality. The effectiveness of AI models largely depends on the quality of the data used for training and validation. Inaccurate, incomplete, or biased data can lead to flawed model predictions and assessments, resulting in misguided decision-making. Organizations often underestimate the importance of rigorous data governance practices, which can compromise the reliability of AI outputs. Poor data quality may hinder model performance and heighten existing biases, leading to ethical concerns and potential regulatory violations.
 

AI Model Risk Management Market Analysis

Based on component, the market is segmented into software and services. In 2023, the software segment accounted for over 70% of the market share and is expected to exceed USD 9 billion by 2032. The software segment growth is driven by the increasing demand for automation in risk assessment and monitoring processes.
 

AI-powered software offers advanced analytics capabilities that automate the identification, evaluation, and mitigation of risks associated with AI models. These tools enhance efficiency by streamlining validation processes and providing real-time insights into model performance. Automation reduces the resources needed for risk management and minimizes human error, improving the overall reliability of AI systems.
 

Based on risk, the AI model risk management market is divided into model risk, operational risk, compliance risk, reputational risk, and strategic risk. The model risk segment held around 31% of the market share in 2023. The growing complexity of AI and machine learning models is expected to drive the demand for model risk. As organizations increasingly adopt sophisticated algorithms for various applications, including predictive analytics and decision-making, the associated risks also grow. Complex models can be prone to biases, overfitting, and other performance issues that require thorough scrutiny. This complexity necessitates robust model risk management practices to ensure reliability, transparency, and accountability.
 

The U.S. region accounted for a AI model risk management market share of over 75% in 2023 and is expected to reach around USD 2.5 billion by 2032. As the use of AI becomes more widespread across sectors such as finance, healthcare, and insurance, regulatory bodies are imposing stricter guidelines to ensure accountability, transparency, and ethical use. This heightened focus on compliance necessitates robust risk management frameworks that can adequately assess and validate AI models.
 

Organizations are required to invest in advanced AI model risk management solutions to navigate the complex regulatory landscape effectively. Additionally, North America's strong emphasis on innovation and technological advancement drives the demand for sophisticated risk management tools that can adapt to the evolving landscape.
 

As AI technologies evolve, research institutions and universities in Europe are partnering with businesses to advance the understanding and application of AI in various sectors. These collaborations facilitate the development of innovative methodologies and best practices for model risk management. By integrating cutting-edge research findings into practical applications, companies can enhance their risk management frameworks, ensuring their models are robust and reliable.
 

Rapid expansion of the fintech sector in Asia Pacific is anticipated to drive the AI model risk management market growth. With the rise of innovative financial technologies, many companies in Asia Pacific are leveraging AI models for tasks such as credit scoring, fraud detection, and customer personalization. This growth in fintech leads to increased complexity in AI models, heightening the need for effective risk management frameworks to ensure reliability and compliance.
 

AI Model Risk Management Market Share

IBM, Microsoft, and Google collectively held a market share of over 15% in the AI model risk management industry in 2023. IBM leverages its expertise in AI and cloud computing, offering robust tools such as Watson for automating risk assessment and validation processes. By integrating AI with existing enterprise solutions, IBM helps organizations ensure compliance and manage model risks effectively.
 

Microsoft focuses on enhancing its Azure cloud platform with AI capabilities, providing clients with advanced analytics and governance frameworks. Its partnerships with financial institutions enable tailored solutions that address specific risk management needs, enhancing its competitive edge.
 

Google emphasizes data-driven insights through its Google Cloud Platform, utilizing advanced machine learning algorithms to optimize model performance and risk assessment. By promoting collaboration with developers and researchers, Google fosters innovation in AI risk management, making it an attractive choice for organizations seeking cutting-edge solutions.
 

AI Model Risk Management Market Companies

Major players operating in the AI model risk management industry are:

• Databricks
• DataRobot
• Empowered Systems
• FICO
• Google
• IBM
• MathWorks
• Microsoft
• SAS
• ValidMind

AI Model Risk Management Industry News 

• In August 2024, HITRUST launched AI risk management assessment. The offering provides an essential toolkit for benchmarking and reporting on the AI risk management efforts for any organization using or deploying AI-based technologies such as ML and LLMs. 
   
• In April 2024, Yields partnersed with SMARTER risk management to enhance model risk management solutions. This collaboration is set to enhance support frameworks for financial institutions by leveraging Yields’s advanced technology expertise with SRM’s MRM seasoned advisory services.
   

The  AI model risk management market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Mn/Bn) from 2021 to 2032, for the following segments:

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Market, By Component

• Software
• Services
  • Professional
  • Managed

Market, By Deployment Model

• On-premises
• Cloud

Market, By Risk

• Model risk
• Operational risk
• Compliance risk
• Reputational risk
• Strategic risk

Market, By Application

• Credit risk management
• Fraud detection and prevention
• Algorithmic trading
• Predictive maintenance
• Others

Market, By End Use

• BFSI
• IT & telecom
• Healthcare
• Automotive
• Retail and e-commerce
• Manufacturing
• Government and defense
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • ANZ
  • Southeast Asia
• Latin America
  • Brazil
  • Mexico
  • Argentina
• MEA
  • UAE
  • Saudi Arabia
  • South Africa