Chicago, July 22, 2024 (GLOBE NEWSWIRE) -- The AI Model Risk Management Market size projected to grow from USD 5.7 billion in 2024 to USD 10.5 billion by 2029, at CAGR of 12.9% during the forecast period. according to a new report by MarketsandMarkets™.
Due to various business drivers, the AI Model Risk Management market is expected to grow significantly during the forecast period. The market is experiencing significant growth due to the increasing need to establish robust security protocols, monitor compliance, and respond effectively to emerging threats, the rising need to automate risk assessment for degraded manual errors, and the need to automate model lifecycle, improve efficiency, and surge quality of the final production models.
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AI Model Risk Management Market Dynamics:
Drivers:
- The increasing need for robust security protocols is a key driver.
- Businesses must protect sensitive data and systems in today's digital landscape.
- Compliance with regulatory requirements is crucial for businesses.
Restraints:
- Increased vulnerability to unauthorized access and data theft.
- Compromised model accuracy and decision-making.
Opportunities:
- Generative AI enables automation of compliance audits.
- Reduces manual effort and costs associated with compliance checks.
- Streamlining compliance processes leads to cost reduction.
List of Key Players in AI Model Risk Management Market:
- Microsoft(US)
- IBM(US)
- SAS Institute (US)
- AWS (US)
- H2O.ai (US)
- Google (US)
- LogicGate (US)
- LogicManager (US)
- C3 AI (US)
- MathWorks (US)
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AI Model risk management software is a comprehensive tool designed to help organizations effectively manage and mitigate the potential risks associated with their models. It uses advanced data analytics and modeling techniques to identify and evaluate potential risks, allowing businesses to make more informed decisions. As per Databricks, AI Model Risk Management software involves identifying, assessing, and mitigating risks associated with AI models to ensure their reliability, accuracy, and compliance with regulatory standards. This process is crucial for maintaining the integrity and performance of AI models, especially as they are increasingly used in critical applications across various industries.
Based on the offering, the AI Model Risk Management market is segmented into software and services. The software segment is further segmented into software by type and deployment mode. The software segment is expected to garner a higher revenue during the forecast period. Software tools offer automated, real-time analysis and comprehensive reporting capabilities, which reduce human error. It also enables organizations to comply with the government regulations, enhance accuracy, and line up operations leading to cost savings and improved decision-making. Software is a preferred choice as it can easily adapt to changing regulations in the dynamic changing environment.
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Based on risk type, the segment has been bifurcated into security risk, ethical risk, and operational risk. The security risk segment is expected to garner a higher revenue during the forecast period. The increasing adoption of advanced technologies such as AI and machine learning across industries fulfills the demand for model risk management solutions. Another reason is the presence of a mature financial services industry with regulatory requirements, driving the adoption of robust model risk management solutions. Businesses in North America are aware of the financial and reputational damage that can result from model failures, leading them to invest in thorough model risk management solutions.
Based on application, the segment has been bifurcated into fraud detection and risk reduction, data classification and labeling, sentiment analysis, model inventory management, customer segmentation and targeting, and regulatory compliance monitoring. Sentiment Analysis grows rapidly during the forecast period as it provides valuable insights into market perceptions and investor behavior, which are crucial to predicting potential risks. By analyzing social media, news articles, and other textual data, sentiment analysis helps detect warnings of market shifts and emerging risks. It also enhances predictive accuracy and supports better decision-making through the identification of trends and anomalies in public opinion. As markets become more volatile and interconnected, the ability to measure sentiments in real-time becomes increasingly important for managing risk, which drives the demand for sentiment analysis tools in model risk management.
The AI Model Risk Management market has been segmented into five regions: North America, Europe, the Middle East & Africa, the Asia Pacific, and Latin America. The AI Model risk management market across North America is driven by the presence of a high level of technological infrastructure and expertise, facilitating the integration of advanced AI model risk management tools. Moreover, North America boasts a highly mature market with significant investments in AI technology. Organizations across various sectors, including finance, healthcare, and retail, are leveraging AI extensively.
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