AI Model Risk Management Market

AI Model Risk Management Market Size, Share, Growth Analysis, By Offering (Software Type and Services), Application (Fraud Detection & Risk Reduction, Regulatory Compliance Monitoring), Risk Type, Vertical and Region - Global Industry Forecast to 2029

Report Code: TC 9073 Jul, 2024, by marketsandmarkets.com

[340 Pages Report] The AI Model Risk Management market is projected to grow from USD 5.7 billion in 2024 to USD 10.5 billion by 2029 at a compound annual growth rate (CAGR) of 12.9% during the forecast period period. Due to various business drivers, the AI Model Risk Management market is expected to grow significantly during the forecast period. There is an increasing need to establish robust security protocols, monitor compliance, and respond effectively to emerging threats, a 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.

AI Model Risk Management Market

To know about the assumptions considered for the study, Request for Free Sample Report

AI Model Risk Management Market  Opportunities

To know about the assumptions considered for the study, download the pdf brochure

AI Model Risk Management Market Dynamics

Driver: Increasing need to establish robust security protocols, monitor compliance, and respond effectively to emerging threats

The increasing need to establish robust security protocols, monitor compliance, and respond effectively to emerging threats drives the adoption of model risk management. In today's digital landscape, businesses face constant cybersecurity risks and regulatory requirements. AI Model risk management helps organizations implement strong security measures and protocols to safeguard sensitive data and systems. It ensures compliance with regulations by providing frameworks for assessing and managing risks associated with models used in decision-making processes. By continuously monitoring and updating these models, businesses can identify and respond promptly to new threats or changes in regulatory requirements. This proactive approach not only enhances cybersecurity resilience but also strengthens overall operational and regulatory compliance efforts, making model risk management a crucial component of modern business strategy.

Restraints: Increasing cybersecurity risks such as data breaches and model tampering

The AI Model Risk Management market is restrained by the growing threat of cybersecurity breaches and model tampering, which heightens concerns over the safety and reliability of AI systems. These risks expose critical vulnerabilities in AI models, making them susceptible to unauthorized access, data theft, and malicious alterations, which can significantly compromise model accuracy and decision-making processes. Data breaches and model tampering undermine trust in AI systems, making organizations hesitant to fully embrace AI-driven risk management solutions. According to the National Institute of Standards and Technology (NIST), the frequency of data breaches has increased by 400% over the past decade, which highlights the escalating threat to data security. Additionally, the Federal Trade Commission (FTC) has reported that cyber incidents cost US businesses over USD 50 billion annually, emphasizing the financial risks associated with data breaches. Model tampering, where malicious actors manipulate AI models to produce false outcomes, poses a substantial risk to the integrity and reliability of AI systems. The U.S. Department of Homeland Security (DHS) has identified model tampering as a critical threat, noting that compromised AI models can lead to incorrect risk assessments and decision-making, potentially causing significant harm. These concerns are further corroborated by the European Union Agency for Cybersecurity (ENISA), which states that the complexity and opacity of AI models make them vulnerable to sophisticated attacks, increasing the difficulty of detecting and mitigating tampering. As a result, organizations are wary of adopting AI Model Risk Management software without robust security measures, fearing that potential breaches and tampering could outweigh the benefits.

Opportunity: Emergence of Generative AI for automating compliance audits and efficiently managing risks

The advent of Generative AI to automate compliance audits and effectively manage risks offers significant opportunities for AI Model Risk Management software in the market. By streamlining compliance processes, Generative AI reduces costs, enhances accuracy, and ensures real-time adherence to evolving regulations. It improves risk identification through advanced data analysis and predictive analytics, enabling proactive risk mitigation. This technology supports dynamic risk assessment, keeps risk management strategies current, and provides actionable insights for strategic decision-making. Sectors such as financial services, healthcare, manufacturing, and retail benefit from improved compliance, operational efficiency, and risk management. Additionally, the ability to handle large data volumes and complex risk scenarios allows organizations to scale their risk management operations efficiently. Overall, Generative AI-driven solutions position organizations as innovators, enhancing customer trust, reducing the likelihood of significant adverse events, and providing a competitive market edge. This increased efficiency and reliability make Generative AI-driven solutions highly attractive, enabling organizations to better manage risks, maintain compliance, and gain a competitive edge.

Challenge: Complex model interpretation and validation process 

The complexity of model interpretation and validation processes presents a substantial challenge in adopting AI Model Risk Management software in the market. Deciphering complex AI models entails grasping their decision-making processes, which can be challenging due to the opacity of models such as deep neural networks. Validating these models necessitates rigorous testing across diverse scenarios to ensure consistent and accurate performance across different conditions. Furthermore, the evolving nature of AI models, capable of adapting to new data and operational contexts, introduces further complexity to their interpretation and validation. Continuous monitoring and updates may be necessary to uphold their accuracy and relevance over time, demanding sustained resources and focus.

AI Model Risk Management Market ecosystem

Top Companies in AI Model Risk Management Market

By services, managed services to register for the fastest growing segment during the forecast period.

Managed services are expected to experience significant growth due to several key factors. Firstly, the growing complexity of regulations requires specialized knowledge, making managed services attractive to companies. Secondly, with the increasing adoption of cloud-based services as businesses are shifting their IT infrastructure to cloud-based solutions, they face complexities in managing and optimizing these environments effectively. Lastly, Managed services have advanced tools and technologies that help identify, assess, and reduce risks more effectively. By providing top-notch solutions, the companies keep up with the latest developments.

By vertical, the Healthcare & Life Sciences segment registered the highest CAGR during the forecast period.

The healthcare and life sciences segment has the highest CAGR in AI model risk management due to several key factors. In today’s world, the industry's reliance on AI and machine learning for drug discovery, personalized medicine, and patient care drives the demand for robust risk management frameworks. As healthcare organizations adopt more AI-driven solutions, there is a critical need to ensure these models are accurate, reliable, and compliant with regulatory standards. Moreover, the sensitivity of healthcare data necessitates stringent risk assessment and management to protect patient privacy and maintain trust. This sector's rapid technological advancements and evolving regulatory landscape further contribute to the significant growth in AI model risk management within healthcare and life sciences.

By application, Fraud Detection and Risk Reduction to register the largest market size during the forecast period.

Fraud Detection and Risk Reduction offers essential functionalities that help organizations safeguard their operations and ensure the reliability and compliance of their AI models. These applications enable real-time monitoring of transactions and activities, immediately detecting fraudulent behavior, which is crucial for maintaining the accuracy and effectiveness of AI models. They provide insights into how AI models make decisions, enhancing transparency and allowing stakeholders to understand and trust the AI processes.

By region,  North America to witness the largest market size during the forecast period.

Several key factors contribute to North America having the largest market size in model risk management. The region has large financial institutions and tech companies that heavily invest in advanced technologies, including AI and machine learning models. 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.

North America AI Model Risk Management Market  Size, and Share

List of Top Companies 

The AI Model Risk Management solution and service providers have implemented various types of organic and inorganic growth strategies, such as product upgrades, new product launches, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. Some major players in the AI Model Risk Management market include Microsoft (US), IBM (US), SAS Institute (US), AWS (US), C3 AI (US), H2O.ai(US), Google (US), LogicGate (US), LogicManager (US), MathWorks (US), Alteryx (US), DataBricks (US), Robust Intelligence (US), CIMCON Software (US), Empowered Systems (UK), Mitratech (US), Yields.io (Belgium), MeticStream (US), iManage (US), UpGuard (US), Apparity(US), AuditBoard (US), NAVEX Global (US), Scrut Automation (India), DataTron (US), Krista (US), Fairly AI (Canada), ModelOp (US), Armilla AI (Canada), Crowe (US), and ValidMind(US).

Get online access to the report on the World's First Market Intelligence Cloud

  • Easy to Download Historical Data & Forecast Numbers
  • Company Analysis Dashboard for high growth potential opportunities
  • Research Analyst Access for customization & queries
  • Competitor Analysis with Interactive dashboard
  • Latest News, Updates & Trend analysis
Request Sample

Scope of the Report

Report Metrics

Details

Market size available for years

2019–2029

Base year considered

2023

Forecast period

2024–2029

Forecast units

USD (Billion)

Segments Covered

Offering, Risk Type, Application, Vertical, and Region

Geographies covered

North America, Asia Pacific, Europe, Middle East & Africa, and Latin America

List of Companies covered

Microsoft(US), IBM(US), SAS Institute (US), AWS (US), H2O.ai (US), Google (US), LogicGate (US), LogicManager (US), C3 AI (US), MathWorks (US), Alteryx (US), DataBricks (US), Robust Intelligence (US), CIMCON Software (US), Empowered Systems (UK), Mitratech (US), Yields.io (Belgium), MeticStream (US), iManage (US), UpGuard (US), Apparity (US), AuditBoard (US), NAVEX Global (US), Scrut Automation (India), DataTron (US), Krista (US), Fairly AI (Canada), ModelOp (US), Armilla AI (Canada), Crowe (US), and ValidMind (US).

This research report categorizes the AI Model Risk Management market based on offering, risk type, application,  vertical, and region.

By Offering:
  • Software by Type
    • Model Management
      • Monitoring and Performance
      • Testing and Validation
      • Governance and Compliance
      • Automated Retraining and Deployment
      • Collaborative Development
    • Bias Detection and Fairness Tools
    • Explainable AI Tools
    • Risk Scoring and Stress Testing Tools
    • Security and Privacy Management Tools
  • Software by Deployment Mode
    • Cloud
    • On-Premises
  • Services
    • Professional Services
    • Consulting & Advisory
    • Integration & Deployment
    • Support & Maintenance
    • Training & Education
    • Managed Services
By Risk Type:
  • Security Risk
  • Ethical Risk
  • Operational Risk
By Application:
  • Fraud Detection and Risk Reduction
  • Data Classification and Labelling
  • Sentiment Analysis
  • Model Inventory Management
  • Customer Segmentation and Targeting
  • Regulatory Compliance Monitoring
  • Other Applications
By Vertical:
  • BFSI
    • Credit Risk Assessment
    • Algorithmic Trading
    • Anti-Money Laundering Monitoring
    • Market Risk Analysis
    • Loan Default Prediction
    • Others
  • Retail & eCommerce
    • Demand and Sales Forecasting
    • Customer Churn Prediction
    • Personalized Recommendations
    • Return and Refund Risk Management
    • Customer Lifetime Value Prediction
    • Others
  • Telecom
    • Network Performance Monitoring
    • Customer Experience Management
    • Usage Pattern Analysis
    • Service Reliability Prediction
    • Revenue Assurance
    • Others
  • Manufacturing
    • Predictive Maintenance
    • Quality Control
    • Production Line Risk Management
    • Lean Manufacturing Optimization
    • Others
  • Healthcare & Life Sciences
    • Patient Risk Stratification
    • Predictive Diagnostics
    • Clinical Trial Optimization
    • Drug Safety Monitoring
    • Healthcare Cost Management
    • Others
  • Media & Entertainment
    • Audience Segmentation
    • Content Recommendation Systems
    • Ad Targeting Optimization
    • Engagement Analytics
    • Content Demand Forecasting
    • Others 
  • IT/ITeS
    • IT Infrastructure Risk Management
    • Data Privacy Compliance Monitoring
    • Service Level Agreement Compliance Prediction
    • Incident Response Optimization
    • System Downtime Prediction
    • Project Risk Management
    • Others
  • Government and Public Sector
    • Public health surveillance
    • Disaster response planning
    • Crime prediction and prevention
    • Incident Response Optimization
    • Tax fraud detection
    • Social services eligibility verification
    • Others
  • Other Verticals (Transportation & Logistics, Real Estate, Education, and Energy and Utilities)
By Region:
  • North America
    • US
    • Canada
  • Europe
    • UK
    • Germany
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia and New Zealand (ANZ)
    • ASEAN
    • Rest of Asia Pacific
  • Middle East & Africa
    • Middle East
      • Saudi Arabia
      • UAE
      • Turkey
      • Qatar
      • Rest of the Middle East
    • Africa
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Rest of Latin America

Recent Developments:

  • In July 2023, the partnership between KPMG and Microsoft involves a strategic collaboration to develop and deliver innovative solutions and services that address clients' critical risk, performance, and growth issues. The collaboration is based on Microsoft's Azure, Dynamics, and data platforms, allowing KPMG and Microsoft to work together to build applications on demand, automate manual processes, and continuously analyze information to reduce errors and enhance decision-making capabilities. This partnership aims to help clients transform their businesses by leveraging Microsoft's cloud and AI technologies, and KPMG's expertise in auditing, tax, and advisory service.
  • In May 2024, IBM and Palo Alto Networks announced a strategic partnership to provide AI-powered security solutions. The collaboration aims to integrate AI into cybersecurity, with IBM platforming its internal security solutions and adopting Palo Alto Networks as its preferred cybersecurity partner. The partnership will see IBM integrate its internal security solutions with Palo Alto Networks, enhancing its Cortex XSIAM platform with IBM's Watsonx large language models. Additionally, IBM will train over 1,000 security consultants on Palo Alto Networks products, and both companies will work together to ensure a seamless transition for customers to the Cortex XSIAM platform.
  • In May 2024, Union Bank of India successfully modernized its risk management systems by partnering with SAS Institute. This partnership aimed to enhance and streamline the Bank’s risk operations and reporting through advanced model risk management solutions. The collaboration addressed the data amalgamation and met the regulatory requirements for credit and operational risk while providing an enterprise view of the bank's risk exposure throughout the risk management life cycle.
  • In May 2024, Amazon Web Services (AWS) and CrowdStrike expanded their strategic partnership to accelerate cloud security and AI innovation. As part of partnership, Amazon has unified its cybersecurity protection on the CrowdStrike Falcon platform, protecting its operations from code to cloud and from device to data. CrowdStrike is expanding its use of AWS services, including Amazon Bedrock and AWS SageMaker, to drive innovation in cloud security, SIEM transformation, and novel cybersecurity AI applications. The partnership aims to help organizations build, operate, and secure their businesses by leveraging the combined strengths of both companies in cloud security and AI innovation.
  • In September 2022, C3 AI and Google Cloud formed a strategic partnership to deliver innovative enterprise AI solutions on Google Cloud's secure and sustainable infrastructure. The entire portfolio of C3 AI's applications, including industry-specific solutions, are available on Google Cloud, enabling customers to leverage C3 AI's AI capabilities with Google Cloud's leading AI tools, solutions, and services. The partnership gives customers faster time to value by allowing them to build, deploy, and scale ML models and enterprise AI applications more efficiently using a unified platform. C3 AI and Google Cloud are co-developing new AI-driven applications to address the most pressing needs of multiple industries.

Frequently Asked Questions (FAQ):

To speak to our analyst for a discussion on the above findings, click Speak to Analyst

TABLE OF CONTENTS           
 
1 INTRODUCTION  
    1.1 STUDY OBJECTIVES 
    1.2 MARKET DEFINITION  
           1.2.1 INCLUSIONS AND EXCLUSIONS        
    1.3 MARKET SCOPE 
           1.3.1 MARKET SEGMENTATION
           1.3.2 REGIONS COVERED
           1.3.3 YEARS CONSIDERED 
    1.4 CURRENCY CONSIDERED  
    1.5 STAKEHOLDERS    
 
2 RESEARCH METHODOLOGY 
    2.1 RESEARCH DATA 
           2.1.1 SECONDARY DATA
                    2.1.1.1 Secondary sources
           2.1.2 PRIMARY DATA
                    2.1.2.1 Primary interview with experts
                    2.1.2.2 List of Key Primary Interview Participants
                    2.1.2.3 Breakdown of primaries
                    2.1.2.4 Primary sources
                    2.1.2.5 Key Industry Insights
    2.2 MARKET SIZE ESTIMATION 
           2.2.1 TOP-DOWN APPROACH
           2.2.2 BOTTOM-UP APPROACH
    2.3 DATA TRIANGULATION 
    2.4 RESEARCH ASSUMPTIONS  
    2.5 RISK ASSESSMENT  
    2.6 LIMITATIONS OF THE STUDY 
    2.7 IMPLICATIONS OF THE RECESSION ON GLOBAL AI MODEL RISK MANAGEMENT MARKET 
 
3 EXECUTIVE SUMMARY    
 
4 PREMIUM INSIGHTS 
    4.1 ATTRACTIVE OPPORTUNITIES FOR COMPANIES IN THE AI MODEL RISK MANAGEMENT MARKET 
    4.2 AI MODEL RISK MANAGEMENT MARKET, BY OFFERING 2024 VS. 2029 
    4.3 AI MODEL RISK MANAGEMENT MARKET, BY RISK TYPE, 2024 VS. 2029 
    4.4 AI MODEL RISK MANAGEMENT MARKET, BY APPLICATION, 2024 VS. 2029 
    4.5 AI MODEL RISK MANAGEMENT MARKET, BY VERTICAL, 2024 VS. 2029 
    4.6 AI MODEL RISK MANAGEMENT MARKET, BY REGION, 2024 VS. 2029 
 
5 MARKET OVERVIEW  
    5.1 MARKET DYNAMICS 
           5.1.1 DRIVERS
           5.1.2 RESTRAINTS
           5.1.3 OPPORTUNITIES
           5.1.4 CHALLENGES
    5.2 INDUSTRY TRENDS 
           5.2.1 AI MODEL RISK MANAGEMENT MARKET: ARCHITECTURE 
           5.2.2 AI MODEL RISK MANAGEMENT MARKET: EVOLUTION
           5.2.3 VALUE / SUPPLY CHAIN ANALYSIS
           5.2.4 ECOSYSTEM ANALYSIS 
           5.2.5 PRICING ANALYSIS 
                    5.2.5.1  AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
                    5.2.5.2  INDICATIVE PRICING ANALYSIS, BY APPLICATION
           5.2.6 PORTER’S FIVE FORCES’ ANALYSIS
                    5.2.6.1 THREAT FROM NEW ENTRANTS
                    5.2.6.2 THREAT OF SUBSTITUTES
                    5.2.6.3 BARGAINING POWER OF SUPPLIERS
                    5.2.6.4 BARGAINING POWER OF BUYERS
                    5.2.6.5 INTENSITY OF COMPETITION RIVALRY
           5.2.7 TRENDS/DISRUPTIONS IMPACTING CUSTOMER’S BUSINESS
           5.2.8 TECHNOLOGY ANALYSIS
                    5.2.8.1 KEY TECHNOLOGIES
                               5.2.8.1.1 AI AND ML
                                             5.2.8.1.1.1  NLP
                               5.2.8.1.2 BIG DATA & ANALYTICS 
                    5.2.8.2 COMPLIMENTARY TECHNOLOGIES
                               5.2.8.2.1  CLOUD COMPUTING
                               5.2.8.2.2  EDGE COMPUTING
                    5.2.8.3 ADJACENT TECHNOLOGIES
                               5.2.8.3.1  COMPUTER VISION 
                               5.2.8.3.2   IOT
                               5.2.8.3.3 RPA
                               5.2.8.3.4 CYBERSECURITY
           5.2.9 CASE STUDY ANALYSIS
                    5.2.9.1 CASE STUDY 1
                    5.2.9.2 CASE STUDY 2
                    5.2.9.3  CASE STUDY 3
           5.2.10 PATENT ANALYSIS
                    5.2.10.1 METHODOLOGY
                    5.2.10.2 PATENTS FILED, BY DOCUMENT TYPE, 2013-2023
                    5.2.10.3 INNOVATION AND PATENT APPLICATIONS
                    5.2.10.4 TOP APPLICANTS
           5.2.11 KEY CONFERENCES & EVENTS, 2024-2025
           5.2.12 REGULATORY LANDSCAPE
                    5.2.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
                               5.2.12.1.1 NORTH AMERICA
                               5.2.12.1.2 EUROPE
                               5.2.12.1.3 ASIA PACIFIC
                               5.2.12.1.4 MIDDLE EAST & AFRICA
                               5.2.12.1.5 LATIN AMERICA
           5.2.13 KEY STAKEHOLDERS AND BUYING CRITERIA
                    5.2.13.1 KEY STAKEHOLDERS IN THE BUYING PROCESS
                               5.2.13.2   BUYING CRITERIA
           5.2.14 BUSINESS MODEL ANALYSIS: AI MODEL RISK MANAGEMENT MARKET
           5.2.15 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
 
6 AI MODEL RISK MANAGEMENT MARKET, BY OFFERING   
    6.1 INTRODUCTION 
           6.1.1 OFFERING: AI MODEL RISK MANAGEMENT MARKET DRIVERS
    6.2 SOFTWARE BY TYPE 
           6.2.1 MODEL MANAGEMENT   
                    6.2.1.1  MONITORING AND PERFORMANCE
                    6.2.1.2 TESTING AND VALIDATION
                    6.2.1.3 GOVERNANCE AND COMPLIANCE
                    6.2.1.4 AUTOMATED RETRAINING AND DEPLOYMENT
                    6.2.1.5 COLLABORATIVE DEVELOPMENT
           6.2.2 BIAS DETECTION AND FAIRNESS TOOLS
           6.2.3 EXPLAINABLE AI (XAI) TOOLS
           6.2.4  RISK SCORING AND STRESS TESTING TOOLS
           6.2.5 SECURITY AND PRIVACY MANAGEMENT TOOLS
    6.3 SOFTWARE BY DEPLOYMENT MODE 
           6.3.1 CLOUD
           6.3.2 ON-PREMISES 
    6.4 SERVICES 
           6.4.1 PROFESSIONAL SERVICES
                    6.4.1.1  CONSULTING & ADVISORY
                    6.4.1.2  INTEGRATION & DEPLOYMENT
                    6.4.1.3 SUPPORT & MAINTENANCE 
                    6.4.1.4   TRAINING & EDUCATION
           6.4.2 MANAGED SERVICES
 
7 AI MODEL RISK MANAGEMENT MARKET, BY RISK TYPE      
    7.1 INTRODUCTION 
           7.1.1 RISK TYPE: AI MODEL RISK MANAGEMENT MARKET DRIVERS
    7.2 SECURITY RISK  
    7.3 ETHICAL RISK  
    7.4 OPERATIONAL RISK 
 
8 AI MODEL RISK MANAGEMENT MARKET, BY APPLICATION 
    8.1 INTRODUCTION 
           8.1.1 APPLICATION: AI MODEL RISK MANAGEMENT MARKET DRIVERS
    8.2 FRAUD DETECTION AND RISK REDUCTION  
    8.3 DATA CLASSIFICATION AND LABELING 
    8.4 SENTIMENT ANALYSIS  
    8.5 MODEL INVENTORY MANAGEMENT 
    8.6 CUSTOMER SEGMENTATION AND TARGETING  
    8.7 REGULATORY COMPLIANCE MONITORING  
    8.8 OTHER APPLICATIONS (FINANCIAL FORECASTING, PORTFOLIO MANAGEMENT, & ANOMALY DETECTION) 
 
9 AI MODEL RISK MANAGEMENT MARKET, BY VERTICAL 
    9.1 INTRODUCTION 
           9.1.1 VERTICAL: AI MODEL RISK MANAGEMENT MARKET DRIVERS
    9.2 BFSI 
           9.2.1 CREDIT RISK ASSESSMENT
           9.2.2 ALGORITHMIC TRADING
           9.2.3 ANTI-MONEY LAUNDERING (AML) MONITORING
           9.2.4 MARKET RISK ANALYSIS
           9.2.5 LOAN DEFAULT PREDICTION
           9.2.6 OTHERS (RISK-WEIGHTED ASSET OPTIMIZATION, OPERATIONAL RISK MANAGEMENT, INSURANCE CLAIM FRAUD DETECTION, AND LIQUIDITY RISK MANAGEMENT)
    9.3 RETAIL & ECOMMERCE  
           9.3.1 DEMAND AND SALES FORECASTING
           9.3.2 CUSTOMER CHURN PREDICTION
           9.3.3 PERSONALIZED RECOMMENDATIONS
           9.3.4 RETURN AND REFUND RISK MANAGEMENT
           9.3.5 CUSTOMER LIFETIME VALUE PREDICTION
           9.3.6 OTHERS (SUPPLY CHAIN DISRUPTION MANAGEMENT, DYNAMIC PRICING MODELS, AND PROMOTION EFFECTIVENESS ANALYSIS)
    9.4 TELECOM  
           9.4.1 NETWORK PERFORMANCE MONITORING
           9.4.2 CUSTOMER EXPERIENCE MANAGEMENT
           9.4.3 USAGE PATTERN ANALYSIS
           9.4.4 SERVICE RELIABILITY PREDICTION
           9.4.5 REVENUE ASSURANCE
           9.4.6 OTHERS (CUSTOMER ACQUISITION AND RETENTION ANALYSIS, PREDICTIVE NETWORK MAINTENANCE, SERVICE USAGE FORECASTING, CHURN REDUCTION STRATEGIES, AND 5G NETWORK ROLLOUT RISK MANAGEMENT)
    9.5 MANUFACTURING 
           9.5.1 PREDICTIVE MAINTENANCE 
           9.5.2 QUALITY CONTROL
           9.5.3 PRODUCTION LINE RISK MANAGEMENT 
           9.5.4 SUPPLIER RISK ASSESSMENT
           9.5.5 LEAN MANUFACTURING OPTIMIZATION
           9.5.6 OTHERS (PRODUCTION SCHEDULING OPTIMIZATION, WORKFORCE PRODUCTIVITY ANALYSIS, AND ENERGY CONSUMPTION OPTIMIZATION)
    9.6 HEALTHCARE & LIFE SCIENCES 
           9.6.1 PATIENT RISK STRATIFICATION
           9.6.2 PREDICTIVE DIAGNOSTICS
           9.6.3 CLINICAL TRIAL OPTIMIZATION
           9.6.4 DRUG SAFETY MONITORING
           9.6.5 HEALTHCARE COST MANAGEMENT
           9.6.6 OTHERS (TREATMENT EFFICACY PREDICTION, HOSPITAL READMISSION RISK PREDICTION, PATIENT FLOW OPTIMIZATION, AND HEALTHCARE RESOURCE UTILIZATION ANALYSIS)
    9.7 MEDIA & ENTERTAINMENT  
           9.7.1 AUDIENCE SEGMENTATION
           9.7.2 CONTENT RECOMMENDATION SYSTEMS
           9.7.3 AD TARGETING OPTIMIZATION
           9.7.4 ENGAGEMENT ANALYTICS
           9.7.5 CONTENT DEMAND FORECASTING
           9.7.6 OTHERS (SUBSCRIPTION REVENUE FORECASTING, CONTENT CREATION RISK MANAGEMENT, USER ENGAGEMENT PREDICTION, COPYRIGHT INFRINGEMENT DETECTION, AND ADVERTISING REVENUE OPTIMIZATION)
    9.8 IT / ITES  
           9.8.1 IT INFRASTRUCTURE RISK MANAGEMENT
           9.8.2 DATA PRIVACY COMPLIANCE MONITORING
           9.8.3 SERVICE LEVEL AGREEMENT (SLA) COMPLIANCE PREDICTION
           9.8.4 INCIDENT RESPONSE OPTIMIZATION
           9.8.5 SYSTEM DOWNTIME PREDICTION
           9.8.6 PROJECT RISK MANAGEMENT
           9.8.7 OTHERS (SOFTWARE DEFECT PREDICTION, IT OPERATIONS RISK ASSESSMENT, CLOUD MIGRATION RISK ANALYSIS, CAPACITY PLANNING AND MANAGEMENT, CLIENT CONTRACT RISK ASSESSMENT)
    9.9 GOVERNMENT & PUBLIC SECTOR  
           9.8.1 PUBLIC HEALTH SURVEILLANCE
           9.8.2 DISASTER RESPONSE PLANNING
           9.8.3 CRIME PREDICTION AND PREVENTION
           9.8.4 SOCIAL SERVICES ELIGIBILITY VERIFICATION
           9.8.5 ENVIRONMENTAL RISK MANAGEMENT
           9.8.6 OTHERS
    9.10 OTHER VERTICALS (TRANSPORTATION & LOGISTICS, REAL ESTATE, EDUCATION, AND ENERGY & UTILITIES) 
 
10 AI MODEL RISK MANAGEMENT MARKET BY REGION 
     10.1 INTRODUCTION 
     10.2 NORTH AMERICA 
             10.2.1 NORTH AMERICA: AI MODEL RISK MANAGEMENT MARKET DRIVERS
             10.2.2 NORTH AMERICA: IMPACT OF RECESSION
             10.2.4 UNITED STATES
             10.2.5 CANADA
     10.3 EUROPE 
             10.3.1 EUROPE: AI MODEL RISK MANAGEMENT MARKET DRIVERS
             10.3.2 EUROPE: IMPACT OF RECESSION
             10.3.4 UNITED KINGDOM
             10.3.5 GERMANY
             10.3.6 FRANCE
             10.3.7 ITALY
             10.3.8 SPAIN
             10.3.9 REST OF EUROPE (SWITZERLAND, DENMARK, NETHERLANDS, SWEDEN, AND OTHERS)
     10.4 ASIA PACIFIC 
             10.4.1 ASIA PACIFIC: AI MODEL RISK MANAGEMENT MARKET DRIVERS
             10.4.2 ASIA PACIFIC: IMPACT OF RECESSION
             10.4.4 CHINA
             10.4.5 JAPAN
             10.4.6 INDIA
             10.4.7 SOUTH KOREA
             10.4.8 AUSTRALIA & NEW ZEALAND
             10.4.9 ASEAN 
               10.4.10 REST OF ASIA PACIFIC (NEPAL, BHUTAN, BANGLADESH, AND OTHERS)
     10.5 MIDDLE EAST & AFRICA 
             10.5.1 MIDDLE EAST & AFRICA: AI MODEL RISK MANAGEMENT MARKET DRIVERS
             10.5.2 MIDDLE EAST & AFRICA: IMPACT OF RECESSION
             10.5.3 MIDDLE EAST 
                       10.5.3.1 SAUDI ARABIA
                       10.5.3.2 QATAR
                       10.5.3.3 TURKEY
                       10.5.3.4 UAE
                       10.5.3.5 REST OF MIDDLE EAST (ISRAEL, EGYPT, KUWAIT, AND OTHERS)
             10.5.4 AFRICA
     10.6 LATIN AMERICA 
             10.6.1 LATIN AMERICA: AI MODEL RISK MANAGEMENT MARKET DRIVERS
             10.6.2 LATIN AMERICA: IMPACT OF RECESSION
             10.6.3 BRAZIL
             10.6.4 MEXICO
             10.6.5 ARGENTINA
             10.6.6 REST OF LATIN AMERICA (PERU, CHILE, COLOMBIA, AND OTHERS) 
 
11 COMPETITIVE LANDSCAPE 
     11.1 INTRODUCTION 
     11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 
             11.2.1 OVERVIEW OF STRATEGIES DEPLOYED BY KEY AI MODEL RISK MANAGEMENT VENDORS
     11.3 MARKET SHARE ANALYSIS 
             11.3.1 MARKET RANKING ANALYSIS
     11.4 REVENUE ANALYSIS  
     11.5 PRODUCT COMPARATIVE ANALYSIS  
             11.5.1 PRODUCT COMPARATIVE ANALYSIS BY SOFTWARE TYPE
     11.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 
             11.6.1 STARS
             11.6.2 EMERGING LEADERS
             11.6.3 PERVASIVE PLAYERS
             11.6.4 PARTICIPANTS
             11.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
                       11.6.5.1 COMPANY FOOTPRINT
                       11.6.5.2 REGION FOOTPRINT 
                       11.6.5.3 OFFERING FOOTPRINT 
                       11.6.5.4 APPLICATION  FOOTPRINT 
                       11.6.5.5 VERTICAL FOOTPRINT 
     11.7 COMPANY EVALUATION MATRIX: STARTUP/SMES, 2023 
             11.7.1 PROGRESSIVE COMPANIES
             11.7.2 RESPONSIVE COMPANIES
             11.7.3 DYNAMIC COMPANIES
             11.7.4 STARTING BLOCKS
             11.7.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
                       11.7.5.1 DETAILED LIST OF STARTUPS/SMES
                       11.7.5.2 COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
     11.7 COMPANY VALUATION AND FINANCIAL METRICS 
     11.8 COMPETITIVE SCENARIOS AND TRENDS 
             11.8.1 PRODUCT LAUNCHES
             11.8.2 DEALS
             11.8.3 OTHERS 
 
12 COMPANY PROFILES   
     12.1 KEY PLAYERS 
     12.2 GOOGLE  
             12.2.1 BUSINESS OVERVIEW
             12.2.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.2.3 RECENT DEVELOPMENTS
             12.2.4 MNM VIEW
     12.3 AWS 
             12.3.1 BUSINESS OVERVIEW
             12.3.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.3.3 RECENT DEVELOPMENTS
             12.3.4 MNM VIEW
     12.4 MICROSOFT 
             12.4.1 BUSINESS OVERVIEW
             12.4.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.4.3 RECENT DEVELOPMENTS
             12.4.4 MNM VIEW
     12.5 IBM 
             12.5.1 BUSINESS OVERVIEW
             12.5.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.5.3 RECENT DEVELOPMENTS
             12.5.4 MNM VIEW
     12.6 SAS INSTITUTE  
             12.6.1 BUSINESS OVERVIEW
             12.6.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.6.3 RECENT DEVELOPMENTS
             12.6.4 MNM VIEW
     12.7 MODELOP 
             12.7.1 BUSINESS OVERVIEW
             12.7.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.7.3 RECENT DEVELOPMENTS
     12.8 LOGICGATE  
             12.7.1 BUSINESS OVERVIEW
             12.7.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.7.3 RECENT DEVELOPMENTS
     12.9 DATABRICKS 
             12.9.1 BUSINESS OVERVIEW
             12.9.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
             12.9.3 RECENT DEVELOPMENTS
     12.1 H2O.AI 
               12.10.1 BUSINESS OVERVIEW
               12.10.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
               12.10.3 RECENT DEVELOPMENTS
     12.11 ALTERYX 
               12.11.1 BUSINESS OVERVIEW
               12.11.2 PRODUCTS/SOLUTIONS/ SERVICES OFFERED
               12.11.3 RECENT DEVELOPMENTS
     12.12 OTHER PLAYERS 
               12.12.1 EMPOWERED SYSTEMS
               12.12.2 MITRATECH
               12.12.3 YIELDS
               12.12.4 C3 AI
               12.12.5 IMANAGE AI  
               12.12.6 METRICSTREAM 
               12.12.7 UPGUARD 
               12.12.8  APPARITY
               12.12.9 CROWE
               12.12.10  CIMCON SOFTWARE
               12.12.11  MATHWORKS 
               12.12.12  AUDITBOARD
               12.12.13  NAVEX GLOBAL 
     12.13 STARTUP/SMES 
               12.13.1 SCRUT AUTOMATION  
               12.13.2 DATATRON 
               12.13.3 KRISTA
               12.13.4  PROTECHT
               12.13.5  FAIRLY 
               12.13.6  VALIDMIND 
               12.13.7 ROBUST INTELLIGENCE
               12.13.8 ARMILLA AI

The AI Model Risk Management market research study involved extensive secondary sources, directories, journals, and paid databases. Primary sources were mainly industry experts from the core and related industries, preferred AI Model Risk Management providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.

Secondary Research

In the secondary research process, various sources were referred to, for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendors' websites. Additionally, AI Model Risk Management spending of various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on software, services, market classification, and segmentation according to offerings of major players, industry trends related to software, deployment mode,  services, risk type,  application, vertical, and regions, and key developments from both market- and technology-oriented perspectives.

Primary Research

In the primary research process, various primary sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and AI Model Risk Management expertise; related key executives from AI Model Risk Management solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders.

Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from software and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using AI Model Risk Management solutions, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of AI Model Risk Management software and services, which would impact the overall AI Model Risk Management market.

AI Model Risk Management Market  Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

In the bottom-up approach, the adoption rate of AI Model Risk Management software and services among different end users in key countries concerning their regions contributing the most to the market share was identified. For cross-validation, the adoption of AI Model Risk Management software and services among industries and different use cases concerning their regions was identified and extrapolated. Use cases identified in different regions were given weightage for the market size calculation.

Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the AI Model Risk Management market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socioeconomic analysis of each country, strategic vendor analysis of major AI Model Risk Management providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall AI Model Risk Management market size and segments’ size were determined and confirmed using the study.

Global AI Model Risk Management Market Size: Bottom-Up and Top-Down Approach

AI Model Risk Management Market Bottom Up and Top Down Approach

To know about the assumptions considered for the study, Request for Free Sample Report

Data Triangulation

Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the AI Model Risk Management market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socioeconomic analysis of each country, strategic vendor analysis of major AI Model Risk Management providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall AI Model Risk Management market size and segments’ size were determined and confirmed using the study.

Market Definition

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.

STAKEHOLDERS

  • Risk Assessment and Software Developers
  • AI Model Risk Management vendors
  • Risk Managers
  • Cloud Service Providers
  • Consulting service providers
  • Business owners
  • Distributors and value-added Resellers (VARs)
  • Independent software vendors
  • Managed service providers
  • Support and maintenance service providers
  • System Integrators (Sis)/migration service providers
  • OEMs
  • Technology providers

Report Objectives

  • To define, describe, and predict the AI model risk management market by offering (software [by type and deployment mode] and services), risk type, application, vertical, and region
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
  • To analyze the micro markets with respect to individual growth trends, prospects, and their contributions to the total market
  • To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the AI model risk management market
  • To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
  • To forecast the market size of five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To profile key players and comprehensively analyze their market rankings and core competencies
  • To analyze competitive developments, such as partnerships, new product launches, and mergers & acquisitions, in the AI model risk management market
  • To analyze the impact of the recession across all regions in the AI model risk management market

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per your company’s specific needs. The following customization options are available for the report:

Product Analysis

  • Product quadrant, which gives a detailed comparison of the product portfolio of each company.

Geographic Analysis

  • Further breakup of the North American AI Model Risk Management market
  • Further breakup of the European AI Model Risk Management market
  • Further breakup of the Asia Pacific AI Model Risk Management market
  • Further breakup of the Middle Eastern & African AI Model Risk Management market
  • Further breakup of the Latin America AI Model Risk Management market

Company Information

  • Detailed analysis and profiling of additional market players (up to five)
Custom Market Research Services

We will customize the research for you, in case the report listed above does not meet with your exact requirements. Our custom research will comprehensively cover the business information you require to help you arrive at strategic and profitable business decisions.

Request Customization
Report Code
TC 9073
Published ON
Jul, 2024
Choose License Type
BUY NOW
  • SHARE
X
Request Customization
Speak to Analyst
Speak to Analyst
OR FACE-TO-FACE MEETING
PERSONALIZE THIS RESEARCH
  • Triangulate with your Own Data
  • Get Data as per your Format and Definition
  • Gain a Deeper Dive on a Specific Application, Geography, Customer or Competitor
  • Any level of Personalization
REQUEST A FREE CUSTOMIZATION
LET US HELP YOU!
  • What are the Known and Unknown Adjacencies Impacting the AI Model Risk Management Market
  • What will your New Revenue Sources be?
  • Who will be your Top Customer; what will make them switch?
  • Defend your Market Share or Win Competitors
  • Get a Scorecard for Target Partners
CUSTOMIZED WORKSHOP REQUEST
  • Call Us
  • +1-888-600-6441 (Corporate office hours)
  • +1-888-600-6441 (US/Can toll free)
  • +44-800-368-9399 (UK office hours)
CONNECT WITH US
ABOUT TRUST ONLINE
©2024 MarketsandMarkets Research Private Ltd. All rights reserved
DMCA.com Protection Status Website Feedback