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
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. The market is anticipated to grow 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 the model lifecycle, improve efficiency, and surge the quality of the final production models.
“By Software type, the Explainable AI segment registers for the fastest growing market during the forecast period.”
The explainable AI segment has rapidly emerged within the AI Model Risk Management landscape. This growth is due to the growing demand for transparency and trust in AI-powered decision-making processes. As organizations across various industries integrate AI systems into their operations, Explainable AI (XAI) provides insights into how the AI models make decisions, which enables stakeholders to identify potential barriers and errors. Government and regulatory bodies also enact strict guidelines that require organizations to demonstrate fairness, accountability, and transparency. Also, the adoption of AI among industries created a need for effective risk management strategies that can handle the AI model complexities. This not only improves the overall performance of AI models but also enhances the trust and confidence of stakeholders in AI-driven decision-making processes.
“By region, Asia Pacific to register the highest CAGR market during the forecast period.” Asia Pacific is projected to grow at the highest rate during the forecast period due to several factors, such as the increasing adoption of advanced technologies and expanding financial services. The fast-growing economy across the region involves effective risk management systems, like model risk management. Investments in infrastructure and digital upgrades also speed up the demand for advanced risk analysis and compliance tools. Businesses in Asia Pacific aim to stay competitive and meet regulations as markets change, leading to a rising demand for thorough AI model risk management software in the market.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI Model Risk Management market.
The report includes the study of key players offering AI Model Risk Management solutions. It profiles major vendors in the AI Model Risk Management market. The major players in the AI Model Risk Management market include 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).
Research Coverage
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.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants with information on the closest approximations of the revenue numbers for the overall AI Model Risk Management market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market's pulse and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
“By Software type, the Explainable AI segment registers for the fastest growing market during the forecast period.”
The explainable AI segment has rapidly emerged within the AI Model Risk Management landscape. This growth is due to the growing demand for transparency and trust in AI-powered decision-making processes. As organizations across various industries integrate AI systems into their operations, Explainable AI (XAI) provides insights into how the AI models make decisions, which enables stakeholders to identify potential barriers and errors. Government and regulatory bodies also enact strict guidelines that require organizations to demonstrate fairness, accountability, and transparency. Also, the adoption of AI among industries created a need for effective risk management strategies that can handle the AI model complexities. This not only improves the overall performance of AI models but also enhances the trust and confidence of stakeholders in AI-driven decision-making processes.
“By region, Asia Pacific to register the highest CAGR market during the forecast period.” Asia Pacific is projected to grow at the highest rate during the forecast period due to several factors, such as the increasing adoption of advanced technologies and expanding financial services. The fast-growing economy across the region involves effective risk management systems, like model risk management. Investments in infrastructure and digital upgrades also speed up the demand for advanced risk analysis and compliance tools. Businesses in Asia Pacific aim to stay competitive and meet regulations as markets change, leading to a rising demand for thorough AI model risk management software in the market.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI Model Risk Management market.
- By Company: Tier I: 45%, Tier II: 35%, and Tier III: 20%
- By Designation: C-Level Executives: 35%, D-Level Executives: 40%, and Others: 25%
- By Region: North America: 40%, Europe: 30%, Asia Pacific: 20%, Latin America-5%, and
The report includes the study of key players offering AI Model Risk Management solutions. It profiles major vendors in the AI Model Risk Management market. The major players in the AI Model Risk Management market include 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).
Research Coverage
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.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants with information on the closest approximations of the revenue numbers for the overall AI Model Risk Management market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market's pulse and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
- Analysis of key drivers (Rising need to automate risk assessment for degraded manual errors, increasing need to establish robust security protocols, monitor compliance, and respond effectively to emerging threats, and rising need to automate the model lifecycle, improve efficiency, and surge the quality of the final production models), restraints (Increasing cybersecurity risks such as data breaches and model tampering, and stringent Regulations and risk frameworks), opportunities (Emergence of Generative AI for automating compliance audits and efficiently managing risks, and the advent of reinforcement learning and deep learning to handle intricate risk scenarios across the BFSI sector), and challenges (Complex model interpretation and validation process, real-time model monitoring could be time-consuming, and the data privacy issues with AI and ML).
- Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI Model Risk Management market.
- Market Development: Comprehensive information about lucrative markets – the report analyses the AI Model Risk Management market across varied regions.
- Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI Model Risk Management market.
- Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players, including Microsoft (US), IBM (US), SAS Institute (US), AWS (US), Google (US), C3 AI (US), and H2O.ai (US) among others in the AI model risk management market strategies. The report also helps stakeholders understand the pulse of the AI model risk management market and provides them with information on key market drivers, restraints, challenges, and opportunities.
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 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 RESEARCH ASSUMPTIONS
2.6 RISK ASSESSMENT
2.7 RESEARCH LIMITATIONS
2.8 IMPLICATIONS OF GENERATIVE AI ON AI MODEL RISK MANAGEMENT MARKET
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI MODEL RISK MANAGEMENT MARKET
4.2 AI MODEL RISK MANAGEMENT MARKET, BY TOP 3 APPLICATIONS
4.3 NORTH AMERICA: AI MODEL RISK MANAGEMENT MARKET, BY OFFERING AND SERVICE
4.4 AI MODEL RISK MANAGEMENT MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Rising need to automate risk assessment for degraded manual errors
5.2.1.2 Growing necessity to establish robust security protocols, monitor compliance, and respond effectively to emerging threats
5.2.1.3 Increasing requirement to automate model lifecycle, improve efficiency, and ensure high-quality final production models
5.2.2 RESTRAINTS
5.2.2.1 Increasing cybersecurity risks
5.2.2.2 Stringent regulations and risk frameworks
5.2.3 OPPORTUNITIES
5.2.3.1 Emergence of generative AI to automate compliance audits and efficiently manage risks
5.2.3.2 Advent of reinforcement learning and deep learning to handle intricate risk scenarios across BFSI sector
5.2.4 CHALLENGES
5.2.4.1 Complex model interpretation and validation processes
5.2.4.2 Extended development timeline due to technical complexity
5.2.4.3 Data privacy issues with AI and ML
5.3 EVOLUTION OF AI MODEL RISK MANAGEMENT MARKET
5.4 SUPPLY CHAIN ANALYSIS
5.5 ECOSYSTEM ANALYSIS
5.5.1 AI MODEL RISK MANAGEMENT MARKET: SOFTWARE AND SERVICE PROVIDERS
5.5.2 AI MODEL RISK MANAGEMENT MARKET: SOFTWARE PROVIDERS
5.5.3 AI MODEL RISK MANAGEMENT MARKET: SERVICE PROVIDERS
5.5.4 AI MODEL RISK MANAGEMENT MARKET: END USERS
5.5.5 AI MODEL RISK MANAGEMENT MARKET: REGULATORY BODIES
5.6 CASE STUDY ANALYSIS
5.6.1 MITRATECH FACILITATES SHAWBROOK BANK DEPLOY CENTRALIZED PLATFORM FOR MANAGING BUSINESS-CRITICAL SPREADSHEETS
5.6.2 YIELDS EMPOWERED AXA BANK BELGIUM TO EVOLVE DYNAMICALLY AND MEET CHALLENGES OF ITS EXPANDING PORTFOLIO EFFECTIVELY
5.6.3 ERSTE BANK CROATIA ADVANCES RISK MANAGEMENT AND CUSTOMER EXPERIENCE WITH SAS VISUAL ANALYTICS
5.6.4 WORLDREMIT TRANSFORMED ITS RISK MANAGEMENT WITH PROTECHT
5.6.5 AYALON INSURANCE ENHANCES ANTI-MONEY LAUNDERING COMPLIANCE WITH SAS INSTITUTE
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 AI and ML
5.7.1.1.1 NLP
5.7.1.2 Big data & analytics
5.7.2 COMPLEMENTARY TECHNOLOGIES
5.7.2.1 Cloud computing
5.7.2.2 Edge computing
5.7.3 ADJACENT TECHNOLOGIES
5.7.3.1 Computer vision
5.7.3.2 IoT
5.7.3.3 RPA
5.7.3.4 Cybersecurity
5.8 KEY CONFERENCES AND EVENTS (2024–2025)
5.9 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
5.10 REGULATORY LANDSCAPE
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.10.2 REGULATIONS: AI MODEL RISK MANAGEMENT
5.10.2.1 North America
5.10.2.1.1 US
5.10.2.1.2 Canada
5.10.2.2 Europe
5.10.2.2.1 UK
5.10.2.3 Asia Pacific
5.10.2.3.1 India
5.10.2.3.2 Singapore
5.10.2.3.3 Australia
5.10.2.3.4 Hong Kong
5.10.2.4 Middle East & Africa
5.10.2.4.1 UAE
5.10.2.4.2 South Africa
5.10.2.4.3 Saudi Arabia
5.10.2.4.4 Israel
5.10.2.5 Latin America
5.10.2.5.1 Brazil
5.10.2.5.2 Mexico
5.10.2.5.3 Argentina
5.10.2.5.4 Colombia
5.10.2.5.5 Peru
5.11 PATENT ANALYSIS
5.11.1 METHODOLOGY
5.11.2 PATENTS FILED, BY DOCUMENT TYPE
5.11.3 INNOVATIONS AND PATENT APPLICATIONS
5.11.3.1 Top 10 patent applicants
5.12 PRICING ANALYSIS
5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
5.12.2 INDICATIVE PRICING ANALYSIS, BY OFFERING
5.13 PORTER’S FIVE FORCES ANALYSIS
5.13.1 THREAT FROM NEW ENTRANTS
5.13.2 THREAT OF SUBSTITUTES
5.13.3 BARGAINING POWER OF SUPPLIERS
5.13.4 BARGAINING POWER OF BUYERS
5.13.5 INTENSITY OF COMPETITION RIVALRY
5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.15 KEY STAKEHOLDERS AND BUYING CRITERIA
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.15.2 BUYING CRITERIA
6 AI MODEL RISK MANAGEMENT MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: AI MODEL RISK MANAGEMENT MARKET DRIVERS
6.2 SOFTWARE
6.2.1 MODEL MANAGEMENT
6.2.1.1 Model management software assists organizations in risk mitigation to adapt swiftly to evolving regulatory and operational demands
6.2.1.2 Monitoring and performance
6.2.1.3 Testing and validation
6.2.1.4 Governance and compliance
6.2.1.5 Automated retraining and development
6.2.1.6 Collaboration development
6.2.2 BIAS DETECTION AND FAIRNESS TOOLS
6.2.2.1 Bias detection and fairness tools identify and mitigate biases within AI models to ensure equitable and non-discriminatory outcomes
6.2.3 EXPLAINABLE AI TOOLS
6.2.3.1 Explainable AI tools facilitate compliance with regulatory standards, support ethical AI practices, and improve accountability
6.2.4 RISK SCORING AND STRESS TESTING TOOLS
6.2.4.1 Risk scoring and stress testing tools safeguard organizations from unforeseen risks and operational disruptions
6.2.5 SECURITY AND PRIVACY MANAGEMENT TOOLS
6.2.5.1 Growing need to ensure safe and ethical use of AI technologies to drive market
6.2.6 REPORTING AND ANALYTICS TOOLS
6.2.6.1 Advanced reporting and analytics tools enhance AI model risk management
6.3 DEPLOYMENT MODE
6.3.1 ON-PREMISES
6.3.1.1 On-premises deployment offers enterprises maximum control, security, and compliance in AI model risk management
6.3.2 CLOUD
6.3.2.1 Need for scalability, flexibility, and cost-effectiveness to fuel demand for cloud deployment of AI model risk management
6.4 SERVICES
6.4.1 PROFESSIONAL SERVICES
6.4.1.1 Consulting & advisory
6.4.1.1.1 Increasing demand for personalized customer experiences and efficient business operations to spur market growth
6.4.1.2 Integration & deployment
6.4.1.2.1 Integration & deployment services facilitate the seamless incorporation and efficient utilization of AI-powered software systems
6.4.1.3 Support & maintenance
6.4.1.3.1 Support & maintenance services ensure the ongoing reliability, performance, and security of AI model risk management solutions
6.4.1.4 Training & education
6.4.1.4.1 Training and education services enhance model transparency and ensure adherence to ethical guidelines and regulatory requirements
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
7.2.1 SECURITY RISKS IN AI MODEL RISK MANAGEMENT SOFTWARE SAFEGUARD AND ENSURE INTEGRITY AND CONFIDENTIALITY OF AI-DRIVEN PROCESSES
7.3 ETHICAL RISK
7.3.1 AI MODEL RISK MANAGEMENT SOFTWARE ENSURES RESPONSIBLE AI USAGE AND MINIMIZES ETHICAL RISKS LINKED WITH AI TECHNOLOGIES
7.4 OPERATIONAL RISK
7.4.1 OPERATIONAL RISK INVOLVES ADDRESSING SYSTEM FAILURES AND OPTIMIZING AI MODELS TO MAINTAIN EFFECTIVENESS
8 AI MODEL RISK MANAGEMENT MARKET, BY APPLICATION
8.1 INTRODUCTION
8.1.1 APPLICATION: AI MODEL RISK MANAGEMENT MARKET DRIVERS
8.2 SENTIMENT ANALYSIS
8.2.1 SENTIMENT ANALYSIS AIDS BUSINESSES UNDERSTAND CUSTOMER PERCEPTIONS, IDENTIFY EMERGING TRENDS, AND DETECT BRAND REPUTATION RISKS
8.3 FRAUD DETECTION AND RISK REDUCTION
8.3.1 FRAUD DETECTION AND RISK REDUCTION ENHANCE TRUST AND SECURITY IN AI MODELS AMONG INDUSTRIES
8.4 MODEL INVENTORY MANAGEMENT
8.4.1 MODEL INVENTORY ENSURES TRACKING, MONITORING, AND OPTIMIZATION OF AI MODELS FOR RISK MITIGATION
8.5 DATA CLASSIFICATION AND LABELING
8.5.1 DATA CLASSIFICATION AND LABELING IDENTIFY POTENTIAL BIAS AND ENSURE ROBUST GOVERNANCE THROUGHOUT AI LIFECYCLE
8.6 REGULATORY COMPLIANCE MONITORING
8.6.1 NEED TO ADHERE TO LEGAL AND ETHICAL STANDARDS IN AI DEPLOYMENT TO DRIVE MARKET
8.7 CUSTOMER SEGMENTATION AND TARGETING
8.7.1 NEED TO EFFECTIVELY ADDRESS DIVERSE CUSTOMER NEEDS TO DRIVE MARKET
8.8 OTHER APPLICATIONS
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 INCREASING COMPLEXITY OF FINANCIAL PRODUCTS AND REGULATIONS TO DRIVE MARKET
9.2.2 CREDIT RISK ASSESSMENT
9.2.3 ALGORITHMIC TRADING
9.2.4 ANTI-MONEY LAUNDERING (AML) MONITORING
9.2.5 MARKET RISK ANALYSIS
9.2.6 LOAN DEFAULT PREDICTION
9.2.7 OTHERS
9.3 RETAIL & ECOMMERCE
9.3.1 AI-DRIVEN RISK MANAGEMENT EMPOWERS BUSINESSES TO MANAGE RISKS AND DELIVER SECURE AND PERSONALIZED CUSTOMER EXPERIENCE
9.3.2 DEMAND AND SALES FORECASTING
9.3.3 CUSTOMER CHURN PREDICTION
9.3.4 PERSONALIZED RECOMMENDATIONS
9.3.5 RETURN AND REFUND RISK MANAGEMENT
9.3.6 CUSTOMER LIFETIME VALUE PREDICTION
9.3.7 OTHERS
9.4 TELECOM
9.4.1 TELECOM INCORPORATES AI MODELS TO MITIGATE RISKS RELATED TO DATA PRIVACY AND NETWORK SECURITY
9.4.2 NETWORK PERFORMANCE MONITORING
9.4.3 CUSTOMER EXPERIENCE MANAGEMENT
9.4.4 USAGE PATTERN ANALYSIS
9.4.5 SERVICE RELIABILITY PREDICTION
9.4.6 REVENUE ASSURANCE
9.4.7 OTHERS
9.5 MANUFACTURING
9.5.1 MANUFACTURING SECTOR USES DATA ANALYTICS TO PREDICT OPERATIONAL RISKS AND ENHANCE PRODUCTION PROCESSES
9.5.2 PREDICTIVE MAINTENANCE
9.5.3 QUALITY CONTROL
9.5.4 PRODUCTION LINE RISK MANAGEMENT
9.5.5 SUPPLIER RISK ASSESSMENT
9.5.6 LEAN MANUFACTURING OPTIMIZATION
9.5.7 OTHERS
9.6 HEALTHCARE & LIFE SCIENCES
9.6.1 ACCURACY, ROBUSTNESS, AND FAIRNESS OF PREDICTIONS TO DRIVE DEMAND IN HEALTHCARE & LIFE SCIENCES
9.6.2 PATIENT RISK STRATIFICATION
9.6.3 PREDICTIVE DIAGNOSTICS
9.6.4 CLINICAL TRIAL OPTIMIZATION
9.6.5 DRUG SAFETY MONITORING
9.6.6 HEALTHCARE COST MANAGEMENT
9.6.7 OTHERS
9.7 MEDIA & ENTERTAINMENT
9.7.1 NEED TO ENHANCE USER EXPERIENCES, MAINTAIN PUBLIC TRUST, AND UPHOLD ETHICAL STANDARDS TO DRIVE DEMAND IN MEDIA & ENTERTAINMENT
9.7.2 AUDIENCE SEGMENTATION
9.7.3 CONTENT RECOMMENDATION SYSTEMS
9.7.4 AD TARGETING OPTIMIZATION
9.7.5 ENGAGEMENT ANALYSIS
9.7.6 CONTENT DEMAND FORECASTING
9.7.7 OTHERS
9.8 IT & ITES
9.8.1 IT & ITES LEVERAGE ADVANCED ANALYTICS TO ASSESS AND MITIGATE RISKS
9.8.2 IT INFRASTRUCTURE RISK MANAGEMENT
9.8.3 DATA PRIVACY COMPLIANCE MONITORING
9.8.4 SERVICE LEVEL AGREEMENT (SLA) COMPLIANCE PREDICTION
9.8.5 INCIDENT RESPONSE OPTIMIZATION
9.8.6 SYSTEM DOWNTIME PREDICTION
9.8.7 PROJECT RISK MANAGEMENT
9.8.8 OTHERS
9.9 GOVERNMENT & PUBLIC SECTOR
9.9.1 GOVERNMENTS INCREASINGLY RELY ON AI FOR DECISION-MAKING IN PUBLIC SAFETY, HEALTHCARE, TRANSPORTATION, AND SOCIAL SERVICES
9.9.2 PUBLIC HEALTH SURVEILLANCE
9.9.3 DISASTER RESPONSE PLANNING
9.9.4 CRIME PREDICTION AND PREVENTION
9.9.5 ENVIRONMENTAL RISK MANAGEMENT
9.9.6 SOCIAL SERVICES ELIGIBILITY VERIFICATION
9.9.7 OTHERS
9.10 OTHER VERTICALS
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
10.2.3 US
10.2.3.1 Rising adoption of AI in finance and banking sectors to drive market
10.2.4 CANADA
10.2.4.1 Evolving regulations and guidelines on model risk management to drive market
10.3 EUROPE
10.3.1 EUROPE: AI MODEL RISK MANAGEMENT MARKET DRIVERS
10.3.2 EUROPE: IMPACT OF RECESSION
10.3.3 UK
10.3.3.1 Evolving Landscape of AI Model Risk Management to address the multifaceted challenges posed by AI-driven decision-making systems in various sectors
10.3.4 GERMANY
10.3.4.1 Growing complexity of AI applications and increasing regulatory scrutiny to drive market
10.3.5 FRANCE
10.3.5.1 Introduction of guidelines and frameworks for responsible development and deployment of AI systems to drive market
10.3.6 SPAIN
10.3.6.1 Increasing integration of advanced machine learning algorithms and AI-powered tools to drive market
10.3.7 ITALY
10.3.7.1 Growing development and adoption of AI technologies to drive market
10.3.8 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: AI MODEL RISK MANAGEMENT MARKET DRIVERS
10.4.2 ASIA PACIFIC: IMPACT OF RECESSION
10.4.3 CHINA
10.4.3.1 Government initiatives and advancements by major tech companies to drive market
10.4.4 JAPAN
10.4.4.1 Focus on mitigating risks related to bias, data privacy, and decision-making to drive market
10.4.5 INDIA
10.4.5.1 Growing adoption of AI technologies in various industries to drive market
10.4.6 SOUTH KOREA
10.4.6.1 Commitment to fostering secure and ethical AI ecosystem to drive market
10.4.7 AUSTRALIA & NEW ZEALAND
10.4.7.1 Rising need for transparency in AI decision-making and demand for robust and reliable AI systems to drive market
10.4.8 ASEAN COUNTRIES
10.4.8.1 Development and implementation of strategies to harness benefits and manage risks of AI to drive market
10.4.9 REST OF ASIA PACIFIC
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
10.5.3.1.1 Ongoing efforts to refine regulatory frameworks, enhance technological capabilities, and foster collaboration to drive market
10.5.3.2 UAE
10.5.3.2.1 Rising adoption of AI and machine learning technologies in financial sector to drive market
10.5.3.3 Qatar
10.5.3.3.1 Increasing focus on robust regulatory frameworks and advanced technological capabilities to mitigate AI-related risks to drive market
10.5.3.4 Turkey
10.5.3.4.1 Investment in AI and machine learning technologies to drive market
10.5.4 REST OF MIDDLE EAST
10.5.5 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
10.6.3.1 Government-led projects and public-private partnerships focused on use of AI in public services to drive market
10.6.4 MEXICO
10.6.4.1 Development of policies and frameworks to regulate AI use to drive market
10.6.5 ARGENTINA
10.6.5.1 Growing focus on developing secure and reliable AI solutions for various sectors to drive market
10.6.6 REST OF LATIN AMERICA
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
11.3 REVENUE ANALYSIS
11.4 MARKET SHARE ANALYSIS
11.4.1 MARKET RANKING ANALYSIS
11.5 PRODUCT COMPARATIVE ANALYSIS
11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
11.7.1 STARS
11.7.2 EMERGING LEADERS
11.7.3 PERVASIVE PLAYERS
11.7.4 PARTICIPANTS
11.7.5 COMPANY FOOTPRINT: KEY PLAYERS
11.7.5.1 Company footprint
11.7.5.2 Regional footprint
11.7.5.3 Application footprint
11.7.5.4 Vertical footprint
11.7.5.5 Product footprint
11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
11.8.1 PROGRESSIVE COMPANIES
11.8.2 RESPONSIVE COMPANIES
11.8.3 DYNAMIC COMPANIES
11.8.4 STARTING BLOCKS
11.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
11.8.5.1 Detailed list of key startups/SMEs
11.8.5.2 Competitive benchmarking of key startups/SMEs
11.9 COMPETITIVE SCENARIO AND TRENDS
11.9.1 PRODUCT LAUNCHES & ENHANCEMENTS
11.9.2 DEALS
12 COMPANY PROFILES
12.1 INTRODUCTION
12.2 KEY PLAYERS
12.2.1 MICROSOFT
12.2.1.1 Business overview
12.2.1.2 Products/Solutions/Services offered
12.2.1.3 Recent developments
12.2.1.4 MnM view
12.2.1.4.1 Key strengths
12.2.1.4.2 Strategic choices
12.2.1.4.3 Weaknesses and competitive threats
12.2.2 IBM
12.2.2.1 Business overview
12.2.2.2 Products/Solutions/Services offered
12.2.2.3 Recent developments
12.2.2.4 MnM view
12.2.2.4.1 Key strengths
12.2.2.4.2 Strategic choices
12.2.2.4.3 Weaknesses and competitive threats
12.2.3 SAS INSTITUTE
12.2.3.1 Business overview
12.2.3.2 Products/Solutions/Services offered
12.2.3.3 Recent developments
12.2.3.4 MnM view
12.2.3.4.1 Key strengths
12.2.3.4.2 Strategic choices
12.2.3.4.3 Weaknesses and competitive threats
12.2.4 AWS
12.2.4.1 Business overview
12.2.4.2 Products/Solutions/Services offered
12.2.4.3 Recent developments
12.2.4.4 MnM view
12.2.4.4.1 Key strengths
12.2.4.4.2 Strategic choices
12.2.4.4.3 Weaknesses and competitive threats
12.2.5 GOOGLE
12.2.5.1 Business overview
12.2.5.2 Products/Solutions/Services offered
12.2.5.3 MnM view
12.2.5.3.1 Key strengths
12.2.5.3.2 Strategic choices
12.2.5.3.3 Weaknesses and competitive threats
12.2.6 H2O.AI
12.2.6.1 Business overview
12.2.6.2 Products/Solutions/Services offered
12.2.6.3 Recent developments
12.2.7 LOGICGATE
12.2.7.1 Business overview
12.2.7.2 Products/Solutions/Services offered
12.2.8 LOGICMANAGER
12.2.8.1 Business overview
12.2.8.2 Products/Solutions/Services offered
12.2.9 C3 AI
12.2.9.1 Business overview
12.2.9.2 Products/Solutions/Services offered
12.2.10 MATHWORKS
12.2.10.1 Business overview
12.2.10.2 Products/Solutions/Services offered
12.2.11 ALTERYX
12.2.12 AUDITBOARD
12.2.13 DATABRICKS
12.2.14 APPARITY
12.2.15 CIMCON SOFTWARE
12.2.16 EMPOWERED SYSTEMS
12.2.17 MITRATECH
12.2.18 NAVEX GLOBAL
12.2.19 CROWE
12.2.20 METRICSTREAM
12.2.21 IMANAGE
12.2.22 UPGUARD
12.3 STARTUPS/SMES
12.3.1 ROBUST INTELLIGENCE
12.3.2 YIELDS.IO
12.3.3 SCRUT AUTOMATION
12.3.4 DATATRON
12.3.5 KRISTA
12.3.6 FAIRLY AI
12.3.7 MODELOP
12.3.8 ARMILLA AI
12.3.9 VALIDMIND
13 ADJACENT AND RELATED MARKETS
13.1 INTRODUCTION
13.2 GENERATIVE AI MARKET - GLOBAL FORECAST TO 2030
13.2.1 MARKET DEFINITION
13.2.2 MARKET OVERVIEW
13.2.2.1 Generative AI market, by offering
13.2.2.2 Generative AI market, by data modality
13.2.2.3 Generative AI market, by application
13.2.2.4 Generative AI market, by vertical
13.2.2.5 Generative AI market, by region
13.3 MLOPS MARKET - GLOBAL FORECAST TO 2027
13.3.1 MARKET DEFINITION
13.3.2 MARKET OVERVIEW
13.3.2.1 MLOps market, by component
13.3.2.2 MLOps market, by deployment mode
13.3.2.3 MLOps market, by organization size
13.3.2.4 MLOps market, by vertical
13.3.2.5 MLOps market, by region
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS
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 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 RESEARCH ASSUMPTIONS
2.6 RISK ASSESSMENT
2.7 RESEARCH LIMITATIONS
2.8 IMPLICATIONS OF GENERATIVE AI ON AI MODEL RISK MANAGEMENT MARKET
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI MODEL RISK MANAGEMENT MARKET
4.2 AI MODEL RISK MANAGEMENT MARKET, BY TOP 3 APPLICATIONS
4.3 NORTH AMERICA: AI MODEL RISK MANAGEMENT MARKET, BY OFFERING AND SERVICE
4.4 AI MODEL RISK MANAGEMENT MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Rising need to automate risk assessment for degraded manual errors
5.2.1.2 Growing necessity to establish robust security protocols, monitor compliance, and respond effectively to emerging threats
5.2.1.3 Increasing requirement to automate model lifecycle, improve efficiency, and ensure high-quality final production models
5.2.2 RESTRAINTS
5.2.2.1 Increasing cybersecurity risks
5.2.2.2 Stringent regulations and risk frameworks
5.2.3 OPPORTUNITIES
5.2.3.1 Emergence of generative AI to automate compliance audits and efficiently manage risks
5.2.3.2 Advent of reinforcement learning and deep learning to handle intricate risk scenarios across BFSI sector
5.2.4 CHALLENGES
5.2.4.1 Complex model interpretation and validation processes
5.2.4.2 Extended development timeline due to technical complexity
5.2.4.3 Data privacy issues with AI and ML
5.3 EVOLUTION OF AI MODEL RISK MANAGEMENT MARKET
5.4 SUPPLY CHAIN ANALYSIS
5.5 ECOSYSTEM ANALYSIS
5.5.1 AI MODEL RISK MANAGEMENT MARKET: SOFTWARE AND SERVICE PROVIDERS
5.5.2 AI MODEL RISK MANAGEMENT MARKET: SOFTWARE PROVIDERS
5.5.3 AI MODEL RISK MANAGEMENT MARKET: SERVICE PROVIDERS
5.5.4 AI MODEL RISK MANAGEMENT MARKET: END USERS
5.5.5 AI MODEL RISK MANAGEMENT MARKET: REGULATORY BODIES
5.6 CASE STUDY ANALYSIS
5.6.1 MITRATECH FACILITATES SHAWBROOK BANK DEPLOY CENTRALIZED PLATFORM FOR MANAGING BUSINESS-CRITICAL SPREADSHEETS
5.6.2 YIELDS EMPOWERED AXA BANK BELGIUM TO EVOLVE DYNAMICALLY AND MEET CHALLENGES OF ITS EXPANDING PORTFOLIO EFFECTIVELY
5.6.3 ERSTE BANK CROATIA ADVANCES RISK MANAGEMENT AND CUSTOMER EXPERIENCE WITH SAS VISUAL ANALYTICS
5.6.4 WORLDREMIT TRANSFORMED ITS RISK MANAGEMENT WITH PROTECHT
5.6.5 AYALON INSURANCE ENHANCES ANTI-MONEY LAUNDERING COMPLIANCE WITH SAS INSTITUTE
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 AI and ML
5.7.1.1.1 NLP
5.7.1.2 Big data & analytics
5.7.2 COMPLEMENTARY TECHNOLOGIES
5.7.2.1 Cloud computing
5.7.2.2 Edge computing
5.7.3 ADJACENT TECHNOLOGIES
5.7.3.1 Computer vision
5.7.3.2 IoT
5.7.3.3 RPA
5.7.3.4 Cybersecurity
5.8 KEY CONFERENCES AND EVENTS (2024–2025)
5.9 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
5.10 REGULATORY LANDSCAPE
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.10.2 REGULATIONS: AI MODEL RISK MANAGEMENT
5.10.2.1 North America
5.10.2.1.1 US
5.10.2.1.2 Canada
5.10.2.2 Europe
5.10.2.2.1 UK
5.10.2.3 Asia Pacific
5.10.2.3.1 India
5.10.2.3.2 Singapore
5.10.2.3.3 Australia
5.10.2.3.4 Hong Kong
5.10.2.4 Middle East & Africa
5.10.2.4.1 UAE
5.10.2.4.2 South Africa
5.10.2.4.3 Saudi Arabia
5.10.2.4.4 Israel
5.10.2.5 Latin America
5.10.2.5.1 Brazil
5.10.2.5.2 Mexico
5.10.2.5.3 Argentina
5.10.2.5.4 Colombia
5.10.2.5.5 Peru
5.11 PATENT ANALYSIS
5.11.1 METHODOLOGY
5.11.2 PATENTS FILED, BY DOCUMENT TYPE
5.11.3 INNOVATIONS AND PATENT APPLICATIONS
5.11.3.1 Top 10 patent applicants
5.12 PRICING ANALYSIS
5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
5.12.2 INDICATIVE PRICING ANALYSIS, BY OFFERING
5.13 PORTER’S FIVE FORCES ANALYSIS
5.13.1 THREAT FROM NEW ENTRANTS
5.13.2 THREAT OF SUBSTITUTES
5.13.3 BARGAINING POWER OF SUPPLIERS
5.13.4 BARGAINING POWER OF BUYERS
5.13.5 INTENSITY OF COMPETITION RIVALRY
5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.15 KEY STAKEHOLDERS AND BUYING CRITERIA
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.15.2 BUYING CRITERIA
6 AI MODEL RISK MANAGEMENT MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: AI MODEL RISK MANAGEMENT MARKET DRIVERS
6.2 SOFTWARE
6.2.1 MODEL MANAGEMENT
6.2.1.1 Model management software assists organizations in risk mitigation to adapt swiftly to evolving regulatory and operational demands
6.2.1.2 Monitoring and performance
6.2.1.3 Testing and validation
6.2.1.4 Governance and compliance
6.2.1.5 Automated retraining and development
6.2.1.6 Collaboration development
6.2.2 BIAS DETECTION AND FAIRNESS TOOLS
6.2.2.1 Bias detection and fairness tools identify and mitigate biases within AI models to ensure equitable and non-discriminatory outcomes
6.2.3 EXPLAINABLE AI TOOLS
6.2.3.1 Explainable AI tools facilitate compliance with regulatory standards, support ethical AI practices, and improve accountability
6.2.4 RISK SCORING AND STRESS TESTING TOOLS
6.2.4.1 Risk scoring and stress testing tools safeguard organizations from unforeseen risks and operational disruptions
6.2.5 SECURITY AND PRIVACY MANAGEMENT TOOLS
6.2.5.1 Growing need to ensure safe and ethical use of AI technologies to drive market
6.2.6 REPORTING AND ANALYTICS TOOLS
6.2.6.1 Advanced reporting and analytics tools enhance AI model risk management
6.3 DEPLOYMENT MODE
6.3.1 ON-PREMISES
6.3.1.1 On-premises deployment offers enterprises maximum control, security, and compliance in AI model risk management
6.3.2 CLOUD
6.3.2.1 Need for scalability, flexibility, and cost-effectiveness to fuel demand for cloud deployment of AI model risk management
6.4 SERVICES
6.4.1 PROFESSIONAL SERVICES
6.4.1.1 Consulting & advisory
6.4.1.1.1 Increasing demand for personalized customer experiences and efficient business operations to spur market growth
6.4.1.2 Integration & deployment
6.4.1.2.1 Integration & deployment services facilitate the seamless incorporation and efficient utilization of AI-powered software systems
6.4.1.3 Support & maintenance
6.4.1.3.1 Support & maintenance services ensure the ongoing reliability, performance, and security of AI model risk management solutions
6.4.1.4 Training & education
6.4.1.4.1 Training and education services enhance model transparency and ensure adherence to ethical guidelines and regulatory requirements
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
7.2.1 SECURITY RISKS IN AI MODEL RISK MANAGEMENT SOFTWARE SAFEGUARD AND ENSURE INTEGRITY AND CONFIDENTIALITY OF AI-DRIVEN PROCESSES
7.3 ETHICAL RISK
7.3.1 AI MODEL RISK MANAGEMENT SOFTWARE ENSURES RESPONSIBLE AI USAGE AND MINIMIZES ETHICAL RISKS LINKED WITH AI TECHNOLOGIES
7.4 OPERATIONAL RISK
7.4.1 OPERATIONAL RISK INVOLVES ADDRESSING SYSTEM FAILURES AND OPTIMIZING AI MODELS TO MAINTAIN EFFECTIVENESS
8 AI MODEL RISK MANAGEMENT MARKET, BY APPLICATION
8.1 INTRODUCTION
8.1.1 APPLICATION: AI MODEL RISK MANAGEMENT MARKET DRIVERS
8.2 SENTIMENT ANALYSIS
8.2.1 SENTIMENT ANALYSIS AIDS BUSINESSES UNDERSTAND CUSTOMER PERCEPTIONS, IDENTIFY EMERGING TRENDS, AND DETECT BRAND REPUTATION RISKS
8.3 FRAUD DETECTION AND RISK REDUCTION
8.3.1 FRAUD DETECTION AND RISK REDUCTION ENHANCE TRUST AND SECURITY IN AI MODELS AMONG INDUSTRIES
8.4 MODEL INVENTORY MANAGEMENT
8.4.1 MODEL INVENTORY ENSURES TRACKING, MONITORING, AND OPTIMIZATION OF AI MODELS FOR RISK MITIGATION
8.5 DATA CLASSIFICATION AND LABELING
8.5.1 DATA CLASSIFICATION AND LABELING IDENTIFY POTENTIAL BIAS AND ENSURE ROBUST GOVERNANCE THROUGHOUT AI LIFECYCLE
8.6 REGULATORY COMPLIANCE MONITORING
8.6.1 NEED TO ADHERE TO LEGAL AND ETHICAL STANDARDS IN AI DEPLOYMENT TO DRIVE MARKET
8.7 CUSTOMER SEGMENTATION AND TARGETING
8.7.1 NEED TO EFFECTIVELY ADDRESS DIVERSE CUSTOMER NEEDS TO DRIVE MARKET
8.8 OTHER APPLICATIONS
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 INCREASING COMPLEXITY OF FINANCIAL PRODUCTS AND REGULATIONS TO DRIVE MARKET
9.2.2 CREDIT RISK ASSESSMENT
9.2.3 ALGORITHMIC TRADING
9.2.4 ANTI-MONEY LAUNDERING (AML) MONITORING
9.2.5 MARKET RISK ANALYSIS
9.2.6 LOAN DEFAULT PREDICTION
9.2.7 OTHERS
9.3 RETAIL & ECOMMERCE
9.3.1 AI-DRIVEN RISK MANAGEMENT EMPOWERS BUSINESSES TO MANAGE RISKS AND DELIVER SECURE AND PERSONALIZED CUSTOMER EXPERIENCE
9.3.2 DEMAND AND SALES FORECASTING
9.3.3 CUSTOMER CHURN PREDICTION
9.3.4 PERSONALIZED RECOMMENDATIONS
9.3.5 RETURN AND REFUND RISK MANAGEMENT
9.3.6 CUSTOMER LIFETIME VALUE PREDICTION
9.3.7 OTHERS
9.4 TELECOM
9.4.1 TELECOM INCORPORATES AI MODELS TO MITIGATE RISKS RELATED TO DATA PRIVACY AND NETWORK SECURITY
9.4.2 NETWORK PERFORMANCE MONITORING
9.4.3 CUSTOMER EXPERIENCE MANAGEMENT
9.4.4 USAGE PATTERN ANALYSIS
9.4.5 SERVICE RELIABILITY PREDICTION
9.4.6 REVENUE ASSURANCE
9.4.7 OTHERS
9.5 MANUFACTURING
9.5.1 MANUFACTURING SECTOR USES DATA ANALYTICS TO PREDICT OPERATIONAL RISKS AND ENHANCE PRODUCTION PROCESSES
9.5.2 PREDICTIVE MAINTENANCE
9.5.3 QUALITY CONTROL
9.5.4 PRODUCTION LINE RISK MANAGEMENT
9.5.5 SUPPLIER RISK ASSESSMENT
9.5.6 LEAN MANUFACTURING OPTIMIZATION
9.5.7 OTHERS
9.6 HEALTHCARE & LIFE SCIENCES
9.6.1 ACCURACY, ROBUSTNESS, AND FAIRNESS OF PREDICTIONS TO DRIVE DEMAND IN HEALTHCARE & LIFE SCIENCES
9.6.2 PATIENT RISK STRATIFICATION
9.6.3 PREDICTIVE DIAGNOSTICS
9.6.4 CLINICAL TRIAL OPTIMIZATION
9.6.5 DRUG SAFETY MONITORING
9.6.6 HEALTHCARE COST MANAGEMENT
9.6.7 OTHERS
9.7 MEDIA & ENTERTAINMENT
9.7.1 NEED TO ENHANCE USER EXPERIENCES, MAINTAIN PUBLIC TRUST, AND UPHOLD ETHICAL STANDARDS TO DRIVE DEMAND IN MEDIA & ENTERTAINMENT
9.7.2 AUDIENCE SEGMENTATION
9.7.3 CONTENT RECOMMENDATION SYSTEMS
9.7.4 AD TARGETING OPTIMIZATION
9.7.5 ENGAGEMENT ANALYSIS
9.7.6 CONTENT DEMAND FORECASTING
9.7.7 OTHERS
9.8 IT & ITES
9.8.1 IT & ITES LEVERAGE ADVANCED ANALYTICS TO ASSESS AND MITIGATE RISKS
9.8.2 IT INFRASTRUCTURE RISK MANAGEMENT
9.8.3 DATA PRIVACY COMPLIANCE MONITORING
9.8.4 SERVICE LEVEL AGREEMENT (SLA) COMPLIANCE PREDICTION
9.8.5 INCIDENT RESPONSE OPTIMIZATION
9.8.6 SYSTEM DOWNTIME PREDICTION
9.8.7 PROJECT RISK MANAGEMENT
9.8.8 OTHERS
9.9 GOVERNMENT & PUBLIC SECTOR
9.9.1 GOVERNMENTS INCREASINGLY RELY ON AI FOR DECISION-MAKING IN PUBLIC SAFETY, HEALTHCARE, TRANSPORTATION, AND SOCIAL SERVICES
9.9.2 PUBLIC HEALTH SURVEILLANCE
9.9.3 DISASTER RESPONSE PLANNING
9.9.4 CRIME PREDICTION AND PREVENTION
9.9.5 ENVIRONMENTAL RISK MANAGEMENT
9.9.6 SOCIAL SERVICES ELIGIBILITY VERIFICATION
9.9.7 OTHERS
9.10 OTHER VERTICALS
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
10.2.3 US
10.2.3.1 Rising adoption of AI in finance and banking sectors to drive market
10.2.4 CANADA
10.2.4.1 Evolving regulations and guidelines on model risk management to drive market
10.3 EUROPE
10.3.1 EUROPE: AI MODEL RISK MANAGEMENT MARKET DRIVERS
10.3.2 EUROPE: IMPACT OF RECESSION
10.3.3 UK
10.3.3.1 Evolving Landscape of AI Model Risk Management to address the multifaceted challenges posed by AI-driven decision-making systems in various sectors
10.3.4 GERMANY
10.3.4.1 Growing complexity of AI applications and increasing regulatory scrutiny to drive market
10.3.5 FRANCE
10.3.5.1 Introduction of guidelines and frameworks for responsible development and deployment of AI systems to drive market
10.3.6 SPAIN
10.3.6.1 Increasing integration of advanced machine learning algorithms and AI-powered tools to drive market
10.3.7 ITALY
10.3.7.1 Growing development and adoption of AI technologies to drive market
10.3.8 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: AI MODEL RISK MANAGEMENT MARKET DRIVERS
10.4.2 ASIA PACIFIC: IMPACT OF RECESSION
10.4.3 CHINA
10.4.3.1 Government initiatives and advancements by major tech companies to drive market
10.4.4 JAPAN
10.4.4.1 Focus on mitigating risks related to bias, data privacy, and decision-making to drive market
10.4.5 INDIA
10.4.5.1 Growing adoption of AI technologies in various industries to drive market
10.4.6 SOUTH KOREA
10.4.6.1 Commitment to fostering secure and ethical AI ecosystem to drive market
10.4.7 AUSTRALIA & NEW ZEALAND
10.4.7.1 Rising need for transparency in AI decision-making and demand for robust and reliable AI systems to drive market
10.4.8 ASEAN COUNTRIES
10.4.8.1 Development and implementation of strategies to harness benefits and manage risks of AI to drive market
10.4.9 REST OF ASIA PACIFIC
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
10.5.3.1.1 Ongoing efforts to refine regulatory frameworks, enhance technological capabilities, and foster collaboration to drive market
10.5.3.2 UAE
10.5.3.2.1 Rising adoption of AI and machine learning technologies in financial sector to drive market
10.5.3.3 Qatar
10.5.3.3.1 Increasing focus on robust regulatory frameworks and advanced technological capabilities to mitigate AI-related risks to drive market
10.5.3.4 Turkey
10.5.3.4.1 Investment in AI and machine learning technologies to drive market
10.5.4 REST OF MIDDLE EAST
10.5.5 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
10.6.3.1 Government-led projects and public-private partnerships focused on use of AI in public services to drive market
10.6.4 MEXICO
10.6.4.1 Development of policies and frameworks to regulate AI use to drive market
10.6.5 ARGENTINA
10.6.5.1 Growing focus on developing secure and reliable AI solutions for various sectors to drive market
10.6.6 REST OF LATIN AMERICA
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
11.3 REVENUE ANALYSIS
11.4 MARKET SHARE ANALYSIS
11.4.1 MARKET RANKING ANALYSIS
11.5 PRODUCT COMPARATIVE ANALYSIS
11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
11.7.1 STARS
11.7.2 EMERGING LEADERS
11.7.3 PERVASIVE PLAYERS
11.7.4 PARTICIPANTS
11.7.5 COMPANY FOOTPRINT: KEY PLAYERS
11.7.5.1 Company footprint
11.7.5.2 Regional footprint
11.7.5.3 Application footprint
11.7.5.4 Vertical footprint
11.7.5.5 Product footprint
11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
11.8.1 PROGRESSIVE COMPANIES
11.8.2 RESPONSIVE COMPANIES
11.8.3 DYNAMIC COMPANIES
11.8.4 STARTING BLOCKS
11.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
11.8.5.1 Detailed list of key startups/SMEs
11.8.5.2 Competitive benchmarking of key startups/SMEs
11.9 COMPETITIVE SCENARIO AND TRENDS
11.9.1 PRODUCT LAUNCHES & ENHANCEMENTS
11.9.2 DEALS
12 COMPANY PROFILES
12.1 INTRODUCTION
12.2 KEY PLAYERS
12.2.1 MICROSOFT
12.2.1.1 Business overview
12.2.1.2 Products/Solutions/Services offered
12.2.1.3 Recent developments
12.2.1.4 MnM view
12.2.1.4.1 Key strengths
12.2.1.4.2 Strategic choices
12.2.1.4.3 Weaknesses and competitive threats
12.2.2 IBM
12.2.2.1 Business overview
12.2.2.2 Products/Solutions/Services offered
12.2.2.3 Recent developments
12.2.2.4 MnM view
12.2.2.4.1 Key strengths
12.2.2.4.2 Strategic choices
12.2.2.4.3 Weaknesses and competitive threats
12.2.3 SAS INSTITUTE
12.2.3.1 Business overview
12.2.3.2 Products/Solutions/Services offered
12.2.3.3 Recent developments
12.2.3.4 MnM view
12.2.3.4.1 Key strengths
12.2.3.4.2 Strategic choices
12.2.3.4.3 Weaknesses and competitive threats
12.2.4 AWS
12.2.4.1 Business overview
12.2.4.2 Products/Solutions/Services offered
12.2.4.3 Recent developments
12.2.4.4 MnM view
12.2.4.4.1 Key strengths
12.2.4.4.2 Strategic choices
12.2.4.4.3 Weaknesses and competitive threats
12.2.5 GOOGLE
12.2.5.1 Business overview
12.2.5.2 Products/Solutions/Services offered
12.2.5.3 MnM view
12.2.5.3.1 Key strengths
12.2.5.3.2 Strategic choices
12.2.5.3.3 Weaknesses and competitive threats
12.2.6 H2O.AI
12.2.6.1 Business overview
12.2.6.2 Products/Solutions/Services offered
12.2.6.3 Recent developments
12.2.7 LOGICGATE
12.2.7.1 Business overview
12.2.7.2 Products/Solutions/Services offered
12.2.8 LOGICMANAGER
12.2.8.1 Business overview
12.2.8.2 Products/Solutions/Services offered
12.2.9 C3 AI
12.2.9.1 Business overview
12.2.9.2 Products/Solutions/Services offered
12.2.10 MATHWORKS
12.2.10.1 Business overview
12.2.10.2 Products/Solutions/Services offered
12.2.11 ALTERYX
12.2.12 AUDITBOARD
12.2.13 DATABRICKS
12.2.14 APPARITY
12.2.15 CIMCON SOFTWARE
12.2.16 EMPOWERED SYSTEMS
12.2.17 MITRATECH
12.2.18 NAVEX GLOBAL
12.2.19 CROWE
12.2.20 METRICSTREAM
12.2.21 IMANAGE
12.2.22 UPGUARD
12.3 STARTUPS/SMES
12.3.1 ROBUST INTELLIGENCE
12.3.2 YIELDS.IO
12.3.3 SCRUT AUTOMATION
12.3.4 DATATRON
12.3.5 KRISTA
12.3.6 FAIRLY AI
12.3.7 MODELOP
12.3.8 ARMILLA AI
12.3.9 VALIDMIND
13 ADJACENT AND RELATED MARKETS
13.1 INTRODUCTION
13.2 GENERATIVE AI MARKET - GLOBAL FORECAST TO 2030
13.2.1 MARKET DEFINITION
13.2.2 MARKET OVERVIEW
13.2.2.1 Generative AI market, by offering
13.2.2.2 Generative AI market, by data modality
13.2.2.3 Generative AI market, by application
13.2.2.4 Generative AI market, by vertical
13.2.2.5 Generative AI market, by region
13.3 MLOPS MARKET - GLOBAL FORECAST TO 2027
13.3.1 MARKET DEFINITION
13.3.2 MARKET OVERVIEW
13.3.2.1 MLOps market, by component
13.3.2.2 MLOps market, by deployment mode
13.3.2.3 MLOps market, by organization size
13.3.2.4 MLOps market, by vertical
13.3.2.5 MLOps market, by region
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS
