![]() 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 m... もっと見る
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SummaryThe 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. 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 Middle East & Africa- 5% 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. Table of Contents1 INTRODUCTION 311.1 STUDY OBJECTIVES 31 1.2 MARKET DEFINITION 31 1.2.1 INCLUSIONS AND EXCLUSIONS 32 1.3 MARKET SCOPE 32 1.3.1 MARKET SEGMENTATION 33 1.3.2 YEARS CONSIDERED 34 1.4 CURRENCY CONSIDERED 34 1.5 STAKEHOLDERS 35 2 RESEARCH METHODOLOGY 36 2.1 RESEARCH DATA 36 2.1.1 SECONDARY DATA 37 2.1.2 PRIMARY DATA 37 2.1.2.1 Breakup of primary profiles 38 2.1.2.2 Key industry insights 38 2.2 DATA TRIANGULATION 39 2.3 MARKET SIZE ESTIMATION 40 2.3.1 TOP-DOWN APPROACH 40 2.3.2 BOTTOM-UP APPROACH 41 2.4 MARKET FORECAST 44 2.5 RESEARCH ASSUMPTIONS 45 2.6 RISK ASSESSMENT 47 2.7 RESEARCH LIMITATIONS 47 2.8 IMPLICATIONS OF GENERATIVE AI ON AI MODEL RISK MANAGEMENT MARKET 48 3 EXECUTIVE SUMMARY 50 4 PREMIUM INSIGHTS 57 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI MODEL RISK MANAGEMENT MARKET 57 4.2 AI MODEL RISK MANAGEMENT MARKET, BY TOP 3 APPLICATIONS 57 4.3 NORTH AMERICA: AI MODEL RISK MANAGEMENT MARKET, BY OFFERING AND SERVICE 58 4.4 AI MODEL RISK MANAGEMENT MARKET, BY REGION 58 5 MARKET OVERVIEW AND INDUSTRY TRENDS 59 5.1 INTRODUCTION 59 5.2 MARKET DYNAMICS 59 5.2.1 DRIVERS 60 5.2.1.1 Rising need to automate risk assessment for degraded manual errors 60 5.2.1.2 Growing necessity to establish robust security protocols, monitor compliance, and respond effectively to emerging threats 61 5.2.1.3 Increasing requirement to automate model lifecycle, improve efficiency, and ensure high-quality final production models 61 5.2.2 RESTRAINTS 61 5.2.2.1 Increasing cybersecurity risks 61 5.2.2.2 Stringent regulations and risk frameworks 62 5.2.3 OPPORTUNITIES 62 5.2.3.1 Emergence of generative AI to automate compliance audits and efficiently manage risks 62 5.2.3.2 Advent of reinforcement learning and deep learning to handle intricate risk scenarios across BFSI sector 62 5.2.4 CHALLENGES 63 5.2.4.1 Complex model interpretation and validation processes 63 5.2.4.2 Extended development timeline due to technical complexity 63 5.2.4.3 Data privacy issues with AI and ML 63 5.3 EVOLUTION OF AI MODEL RISK MANAGEMENT MARKET 64 5.4 SUPPLY CHAIN ANALYSIS 65 5.5 ECOSYSTEM ANALYSIS 66 5.5.1 AI MODEL RISK MANAGEMENT MARKET: SOFTWARE AND SERVICE PROVIDERS 68 5.5.2 AI MODEL RISK MANAGEMENT MARKET: SOFTWARE PROVIDERS 68 5.5.3 AI MODEL RISK MANAGEMENT MARKET: SERVICE PROVIDERS 69 5.5.4 AI MODEL RISK MANAGEMENT MARKET: END USERS 69 5.5.5 AI MODEL RISK MANAGEMENT MARKET: REGULATORY BODIES 69 5.6 CASE STUDY ANALYSIS 69 5.6.1 MITRATECH FACILITATES SHAWBROOK BANK DEPLOY CENTRALIZED PLATFORM FOR MANAGING BUSINESS-CRITICAL SPREADSHEETS 69 5.6.2 YIELDS EMPOWERED AXA BANK BELGIUM TO EVOLVE DYNAMICALLY AND MEET CHALLENGES OF ITS EXPANDING PORTFOLIO EFFECTIVELY 70 5.6.3 ERSTE BANK CROATIA ADVANCES RISK MANAGEMENT AND CUSTOMER EXPERIENCE WITH SAS VISUAL ANALYTICS 70 5.6.4 WORLDREMIT TRANSFORMED ITS RISK MANAGEMENT WITH PROTECHT 71 5.6.5 AYALON INSURANCE ENHANCES ANTI-MONEY LAUNDERING COMPLIANCE WITH SAS INSTITUTE 72 5.7 TECHNOLOGY ANALYSIS 73 5.7.1 KEY TECHNOLOGIES 73 5.7.1.1 AI and ML 73 5.7.1.1.1 NLP 74 5.7.1.2 Big data & analytics 74 5.7.2 COMPLEMENTARY TECHNOLOGIES 74 5.7.2.1 Cloud computing 74 5.7.2.2 Edge computing 74 5.7.3 ADJACENT TECHNOLOGIES 75 5.7.3.1 Computer vision 75 5.7.3.2 IoT 75 5.7.3.3 RPA 75 5.7.3.4 Cybersecurity 76 5.8 KEY CONFERENCES AND EVENTS (2024–2025) 76 5.9 INVESTMENT LANDSCAPE AND FUNDING SCENARIO 77 5.10 REGULATORY LANDSCAPE 78 5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 78 5.10.2 REGULATIONS: AI MODEL RISK MANAGEMENT 83 5.10.2.1 North America 83 5.10.2.1.1 US 83 5.10.2.1.2 Canada 83 5.10.2.2 Europe 83 5.10.2.2.1 UK 83 5.10.2.3 Asia Pacific 84 5.10.2.3.1 India 84 5.10.2.3.2 Singapore 84 5.10.2.3.3 Australia 84 5.10.2.3.4 Hong Kong 84 5.10.2.4 Middle East & Africa 84 5.10.2.4.1 UAE 84 5.10.2.4.2 South Africa 84 5.10.2.4.3 Saudi Arabia 84 5.10.2.4.4 Israel 84 5.10.2.5 Latin America 85 5.10.2.5.1 Brazil 85 5.10.2.5.2 Mexico 85 5.10.2.5.3 Argentina 85 5.10.2.5.4 Colombia 85 5.10.2.5.5 Peru 85 5.11 PATENT ANALYSIS 85 5.11.1 METHODOLOGY 85 5.11.2 PATENTS FILED, BY DOCUMENT TYPE 86 5.11.3 INNOVATIONS AND PATENT APPLICATIONS 86 5.11.3.1 Top 10 patent applicants 87 5.12 PRICING ANALYSIS 91 5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION 92 5.12.2 INDICATIVE PRICING ANALYSIS, BY OFFERING 93 5.13 PORTER’S FIVE FORCES ANALYSIS 94 5.13.1 THREAT FROM NEW ENTRANTS 95 5.13.2 THREAT OF SUBSTITUTES 95 5.13.3 BARGAINING POWER OF SUPPLIERS 96 5.13.4 BARGAINING POWER OF BUYERS 96 5.13.5 INTENSITY OF COMPETITION RIVALRY 96 5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 97 5.15 KEY STAKEHOLDERS AND BUYING CRITERIA 98 5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS 98 5.15.2 BUYING CRITERIA 99 6 AI MODEL RISK MANAGEMENT MARKET, BY OFFERING 100 6.1 INTRODUCTION 101 6.1.1 OFFERING: AI MODEL RISK MANAGEMENT MARKET DRIVERS 101 6.2 SOFTWARE 102 6.2.1 MODEL MANAGEMENT 103 6.2.1.1 Model management software assists organizations in risk mitigation to adapt swiftly to evolving regulatory and operational demands 103 6.2.1.2 Monitoring and performance 105 6.2.1.3 Testing and validation 106 6.2.1.4 Governance and compliance 107 6.2.1.5 Automated retraining and development 108 6.2.1.6 Collaboration development 109 6.2.2 BIAS DETECTION AND FAIRNESS TOOLS 110 6.2.2.1 Bias detection and fairness tools identify and mitigate biases within AI models to ensure equitable and non-discriminatory outcomes 110 6.2.3 EXPLAINABLE AI TOOLS 111 6.2.3.1 Explainable AI tools facilitate compliance with regulatory standards, support ethical AI practices, and improve accountability 111 6.2.4 RISK SCORING AND STRESS TESTING TOOLS 112 6.2.4.1 Risk scoring and stress testing tools safeguard organizations from unforeseen risks and operational disruptions 112 6.2.5 SECURITY AND PRIVACY MANAGEMENT TOOLS 113 6.2.5.1 Growing need to ensure safe and ethical use of AI technologies to drive market 113 6.2.6 REPORTING AND ANALYTICS TOOLS 114 6.2.6.1 Advanced reporting and analytics tools enhance AI model risk management 114 6.3 DEPLOYMENT MODE 115 6.3.1 ON-PREMISES 117 6.3.1.1 On-premises deployment offers enterprises maximum control, security, and compliance in AI model risk management 117 6.3.2 CLOUD 118 6.3.2.1 Need for scalability, flexibility, and cost-effectiveness to fuel demand for cloud deployment of AI model risk management 118 6.4 SERVICES 119 6.4.1 PROFESSIONAL SERVICES 120 6.4.1.1 Consulting & advisory 122 6.4.1.1.1 Increasing demand for personalized customer experiences and efficient business operations to spur market growth 122 6.4.1.2 Integration & deployment 123 6.4.1.2.1 Integration & deployment services facilitate the seamless incorporation and efficient utilization of AI-powered software systems 123 6.4.1.3 Support & maintenance 124 6.4.1.3.1 Support & maintenance services ensure the ongoing reliability, performance, and security of AI model risk management solutions 124 6.4.1.4 Training & education 125 6.4.1.4.1 Training and education services enhance model transparency and ensure adherence to ethical guidelines and regulatory requirements 125 6.4.2 MANAGED SERVICES 126 7 AI MODEL RISK MANAGEMENT MARKET, BY RISK TYPE 128 7.1 INTRODUCTION 129 7.1.1 RISK TYPE: AI MODEL RISK MANAGEMENT MARKET DRIVERS 129 7.2 SECURITY RISK 130 7.2.1 SECURITY RISKS IN AI MODEL RISK MANAGEMENT SOFTWARE SAFEGUARD AND ENSURE INTEGRITY AND CONFIDENTIALITY OF AI-DRIVEN PROCESSES 130 7.3 ETHICAL RISK 131 7.3.1 AI MODEL RISK MANAGEMENT SOFTWARE ENSURES RESPONSIBLE AI USAGE AND MINIMIZES ETHICAL RISKS LINKED WITH AI TECHNOLOGIES 131 7.4 OPERATIONAL RISK 132 7.4.1 OPERATIONAL RISK INVOLVES ADDRESSING SYSTEM FAILURES AND OPTIMIZING AI MODELS TO MAINTAIN EFFECTIVENESS 132 8 AI MODEL RISK MANAGEMENT MARKET, BY APPLICATION 134 8.1 INTRODUCTION 135 8.1.1 APPLICATION: AI MODEL RISK MANAGEMENT MARKET DRIVERS 135 8.2 SENTIMENT ANALYSIS 137 8.2.1 SENTIMENT ANALYSIS AIDS BUSINESSES UNDERSTAND CUSTOMER PERCEPTIONS, IDENTIFY EMERGING TRENDS, AND DETECT BRAND REPUTATION RISKS 137 8.3 FRAUD DETECTION AND RISK REDUCTION 138 8.3.1 FRAUD DETECTION AND RISK REDUCTION ENHANCE TRUST AND SECURITY IN AI MODELS AMONG INDUSTRIES 138 8.4 MODEL INVENTORY MANAGEMENT 139 8.4.1 MODEL INVENTORY ENSURES TRACKING, MONITORING, AND OPTIMIZATION OF AI MODELS FOR RISK MITIGATION 139 8.5 DATA CLASSIFICATION AND LABELING 140 8.5.1 DATA CLASSIFICATION AND LABELING IDENTIFY POTENTIAL BIAS AND ENSURE ROBUST GOVERNANCE THROUGHOUT AI LIFECYCLE 140 8.6 REGULATORY COMPLIANCE MONITORING 141 8.6.1 NEED TO ADHERE TO LEGAL AND ETHICAL STANDARDS IN AI DEPLOYMENT TO DRIVE MARKET 141 8.7 CUSTOMER SEGMENTATION AND TARGETING 142 8.7.1 NEED TO EFFECTIVELY ADDRESS DIVERSE CUSTOMER NEEDS TO DRIVE MARKET 142 8.8 OTHER APPLICATIONS 143 9 AI MODEL RISK MANAGEMENT MARKET, BY VERTICAL 145 9.1 INTRODUCTION 146 9.1.1 VERTICAL: AI MODEL RISK MANAGEMENT MARKET DRIVERS 146 9.2 BFSI 148 9.2.1 INCREASING COMPLEXITY OF FINANCIAL PRODUCTS AND REGULATIONS TO DRIVE MARKET 148 9.2.2 CREDIT RISK ASSESSMENT 149 9.2.3 ALGORITHMIC TRADING 150 9.2.4 ANTI-MONEY LAUNDERING (AML) MONITORING 150 9.2.5 MARKET RISK ANALYSIS 150 9.2.6 LOAN DEFAULT PREDICTION 151 9.2.7 OTHERS 151 9.3 RETAIL & ECOMMERCE 152 9.3.1 AI-DRIVEN RISK MANAGEMENT EMPOWERS BUSINESSES TO MANAGE RISKS AND DELIVER SECURE AND PERSONALIZED CUSTOMER EXPERIENCE 152 9.3.2 DEMAND AND SALES FORECASTING 153 9.3.3 CUSTOMER CHURN PREDICTION 153 9.3.4 PERSONALIZED RECOMMENDATIONS 154 9.3.5 RETURN AND REFUND RISK MANAGEMENT 154 9.3.6 CUSTOMER LIFETIME VALUE PREDICTION 154 9.3.7 OTHERS 155 9.4 TELECOM 155 9.4.1 TELECOM INCORPORATES AI MODELS TO MITIGATE RISKS RELATED TO DATA PRIVACY AND NETWORK SECURITY 155 9.4.2 NETWORK PERFORMANCE MONITORING 156 9.4.3 CUSTOMER EXPERIENCE MANAGEMENT 157 9.4.4 USAGE PATTERN ANALYSIS 157 9.4.5 SERVICE RELIABILITY PREDICTION 157 9.4.6 REVENUE ASSURANCE 158 9.4.7 OTHERS 158 9.5 MANUFACTURING 159 9.5.1 MANUFACTURING SECTOR USES DATA ANALYTICS TO PREDICT OPERATIONAL RISKS AND ENHANCE PRODUCTION PROCESSES 159 9.5.2 PREDICTIVE MAINTENANCE 160 9.5.3 QUALITY CONTROL 160 9.5.4 PRODUCTION LINE RISK MANAGEMENT 161 9.5.5 SUPPLIER RISK ASSESSMENT 161 9.5.6 LEAN MANUFACTURING OPTIMIZATION 161 9.5.7 OTHERS 162 9.6 HEALTHCARE & LIFE SCIENCES 162 9.6.1 ACCURACY, ROBUSTNESS, AND FAIRNESS OF PREDICTIONS TO DRIVE DEMAND IN HEALTHCARE & LIFE SCIENCES 162 9.6.2 PATIENT RISK STRATIFICATION 163 9.6.3 PREDICTIVE DIAGNOSTICS 164 9.6.4 CLINICAL TRIAL OPTIMIZATION 164 9.6.5 DRUG SAFETY MONITORING 164 9.6.6 HEALTHCARE COST MANAGEMENT 165 9.6.7 OTHERS 165 9.7 MEDIA & ENTERTAINMENT 166 9.7.1 NEED TO ENHANCE USER EXPERIENCES, MAINTAIN PUBLIC TRUST, AND UPHOLD ETHICAL STANDARDS TO DRIVE DEMAND IN MEDIA & ENTERTAINMENT 166 9.7.2 AUDIENCE SEGMENTATION 167 9.7.3 CONTENT RECOMMENDATION SYSTEMS 167 9.7.4 AD TARGETING OPTIMIZATION 168 9.7.5 ENGAGEMENT ANALYSIS 168 9.7.6 CONTENT DEMAND FORECASTING 168 9.7.7 OTHERS 169 9.8 IT & ITES 169 9.8.1 IT & ITES LEVERAGE ADVANCED ANALYTICS TO ASSESS AND MITIGATE RISKS 169 9.8.2 IT INFRASTRUCTURE RISK MANAGEMENT 170 9.8.3 DATA PRIVACY COMPLIANCE MONITORING 171 9.8.4 SERVICE LEVEL AGREEMENT (SLA) COMPLIANCE PREDICTION 171 9.8.5 INCIDENT RESPONSE OPTIMIZATION 171 9.8.6 SYSTEM DOWNTIME PREDICTION 171 9.8.7 PROJECT RISK MANAGEMENT 172 9.8.8 OTHERS 172 9.9 GOVERNMENT & PUBLIC SECTOR 173 9.9.1 GOVERNMENTS INCREASINGLY RELY ON AI FOR DECISION-MAKING IN PUBLIC SAFETY, HEALTHCARE, TRANSPORTATION, AND SOCIAL SERVICES 173 9.9.2 PUBLIC HEALTH SURVEILLANCE 174 9.9.3 DISASTER RESPONSE PLANNING 174 9.9.4 CRIME PREDICTION AND PREVENTION 174 9.9.5 ENVIRONMENTAL RISK MANAGEMENT 174 9.9.6 SOCIAL SERVICES ELIGIBILITY VERIFICATION 174 9.9.7 OTHERS 174 9.10 OTHER VERTICALS 175 10 AI MODEL RISK MANAGEMENT MARKET, BY REGION 177 10.1 INTRODUCTION 178 10.2 NORTH AMERICA 179 10.2.1 NORTH AMERICA: AI MODEL RISK MANAGEMENT MARKET DRIVERS 180 10.2.2 NORTH AMERICA: IMPACT OF RECESSION 180 10.2.3 US 188 10.2.3.1 Rising adoption of AI in finance and banking sectors to drive market 188 10.2.4 CANADA 189 10.2.4.1 Evolving regulations and guidelines on model risk management to drive market 189 10.3 EUROPE 190 10.3.1 EUROPE: AI MODEL RISK MANAGEMENT MARKET DRIVERS 190 10.3.2 EUROPE: IMPACT OF RECESSION 191 10.3.3 UK 198 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 198 10.3.4 GERMANY 199 10.3.4.1 Growing complexity of AI applications and increasing regulatory scrutiny to drive market 199 10.3.5 FRANCE 200 10.3.5.1 Introduction of guidelines and frameworks for responsible development and deployment of AI systems to drive market 200 10.3.6 SPAIN 200 10.3.6.1 Increasing integration of advanced machine learning algorithms and AI-powered tools to drive market 200 10.3.7 ITALY 201 10.3.7.1 Growing development and adoption of AI technologies to drive market 201 10.3.8 REST OF EUROPE 201 10.4 ASIA PACIFIC 202 10.4.1 ASIA PACIFIC: AI MODEL RISK MANAGEMENT MARKET DRIVERS 202 10.4.2 ASIA PACIFIC: IMPACT OF RECESSION 202 10.4.3 CHINA 211 10.4.3.1 Government initiatives and advancements by major tech companies to drive market 211 10.4.4 JAPAN 211 10.4.4.1 Focus on mitigating risks related to bias, data privacy, and decision-making to drive market 211 10.4.5 INDIA 212 10.4.5.1 Growing adoption of AI technologies in various industries to drive market 212 10.4.6 SOUTH KOREA 212 10.4.6.1 Commitment to fostering secure and ethical AI ecosystem to drive market 212 10.4.7 AUSTRALIA & NEW ZEALAND 213 10.4.7.1 Rising need for transparency in AI decision-making and demand for robust and reliable AI systems to drive market 213 10.4.8 ASEAN COUNTRIES 213 10.4.8.1 Development and implementation of strategies to harness benefits and manage risks of AI to drive market 213 10.4.9 REST OF ASIA PACIFIC 214 10.5 MIDDLE EAST & AFRICA 215 10.5.1 MIDDLE EAST & AFRICA: AI MODEL RISK MANAGEMENT MARKET DRIVERS 215 10.5.2 MIDDLE EAST & AFRICA: IMPACT OF RECESSION 215 10.5.3 MIDDLE EAST 223 10.5.3.1 Saudi Arabia 223 10.5.3.1.1 Ongoing efforts to refine regulatory frameworks, enhance technological capabilities, and foster collaboration to drive market 223 10.5.3.2 UAE 224 10.5.3.2.1 Rising adoption of AI and machine learning technologies in financial sector to drive market 224 10.5.3.3 Qatar 225 10.5.3.3.1 Increasing focus on robust regulatory frameworks and advanced technological capabilities to mitigate AI-related risks to drive market 225 10.5.3.4 Turkey 225 10.5.3.4.1 Investment in AI and machine learning technologies to drive market 225 10.5.4 REST OF MIDDLE EAST 226 10.5.5 AFRICA 226 10.6 LATIN AMERICA 227 10.6.1 LATIN AMERICA: AI MODEL RISK MANAGEMENT MARKET DRIVERS 227 10.6.2 LATIN AMERICA: IMPACT OF RECESSION 228 10.6.3 BRAZIL 234 10.6.3.1 Government-led projects and public-private partnerships focused on use of AI in public services to drive market 234 10.6.4 MEXICO 235 10.6.4.1 Development of policies and frameworks to regulate AI use to drive market 235 10.6.5 ARGENTINA 236 10.6.5.1 Growing focus on developing secure and reliable AI solutions for various sectors to drive market 236 10.6.6 REST OF LATIN AMERICA 236 11 COMPETITIVE LANDSCAPE 237 11.1 OVERVIEW 237 11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 237 11.3 REVENUE ANALYSIS 240 11.4 MARKET SHARE ANALYSIS 241 11.4.1 MARKET RANKING ANALYSIS 242 11.5 PRODUCT COMPARATIVE ANALYSIS 243 11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS 244 11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 245 11.7.1 STARS 245 11.7.2 EMERGING LEADERS 245 11.7.3 PERVASIVE PLAYERS 245 11.7.4 PARTICIPANTS 245 11.7.5 COMPANY FOOTPRINT: KEY PLAYERS 247 11.7.5.1 Company footprint 247 11.7.5.2 Regional footprint 248 11.7.5.3 Application footprint 249 11.7.5.4 Vertical footprint 250 11.7.5.5 Product footprint 251 11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 252 11.8.1 PROGRESSIVE COMPANIES 252 11.8.2 RESPONSIVE COMPANIES 252 11.8.3 DYNAMIC COMPANIES 252 11.8.4 STARTING BLOCKS 252 11.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 254 11.8.5.1 Detailed list of key startups/SMEs 254 11.8.5.2 Competitive benchmarking of key startups/SMEs 255 11.9 COMPETITIVE SCENARIO AND TRENDS 255 11.9.1 PRODUCT LAUNCHES & ENHANCEMENTS 255 11.9.2 DEALS 258 12 COMPANY PROFILES 262 12.1 INTRODUCTION 262 12.2 KEY PLAYERS 262 12.2.1 MICROSOFT 262 12.2.1.1 Business overview 262 12.2.1.2 Products/Solutions/Services offered 264 12.2.1.3 Recent developments 264 12.2.1.4 MnM view 265 12.2.1.4.1 Key strengths 265 12.2.1.4.2 Strategic choices 266 12.2.1.4.3 Weaknesses and competitive threats 266 12.2.2 IBM 267 12.2.2.1 Business overview 267 12.2.2.2 Products/Solutions/Services offered 268 12.2.2.3 Recent developments 269 12.2.2.4 MnM view 271 12.2.2.4.1 Key strengths 271 12.2.2.4.2 Strategic choices 271 12.2.2.4.3 Weaknesses and competitive threats 271 12.2.3 SAS INSTITUTE 272 12.2.3.1 Business overview 272 12.2.3.2 Products/Solutions/Services offered 272 12.2.3.3 Recent developments 273 12.2.3.4 MnM view 274 12.2.3.4.1 Key strengths 274 12.2.3.4.2 Strategic choices 274 12.2.3.4.3 Weaknesses and competitive threats 275 12.2.4 AWS 276 12.2.4.1 Business overview 276 12.2.4.2 Products/Solutions/Services offered 277 12.2.4.3 Recent developments 277 12.2.4.4 MnM view 279 12.2.4.4.1 Key strengths 279 12.2.4.4.2 Strategic choices 279 12.2.4.4.3 Weaknesses and competitive threats 280 12.2.5 GOOGLE 281 12.2.5.1 Business overview 281 12.2.5.2 Products/Solutions/Services offered 282 12.2.5.3 MnM view 283 12.2.5.3.1 Key strengths 283 12.2.5.3.2 Strategic choices 283 12.2.5.3.3 Weaknesses and competitive threats 283 12.2.6 H2O.AI 284 12.2.6.1 Business overview 284 12.2.6.2 Products/Solutions/Services offered 284 12.2.6.3 Recent developments 285 12.2.7 LOGICGATE 286 12.2.7.1 Business overview 286 12.2.7.2 Products/Solutions/Services offered 286 12.2.8 LOGICMANAGER 289 12.2.8.1 Business overview 289 12.2.8.2 Products/Solutions/Services offered 289 12.2.9 C3 AI 291 12.2.9.1 Business overview 291 12.2.9.2 Products/Solutions/Services offered 292 12.2.10 MATHWORKS 294 12.2.10.1 Business overview 294 12.2.10.2 Products/Solutions/Services offered 294 12.2.11 ALTERYX 296 12.2.12 AUDITBOARD 297 12.2.13 DATABRICKS 298 12.2.14 APPARITY 299 12.2.15 CIMCON SOFTWARE 300 12.2.16 EMPOWERED SYSTEMS 300 12.2.17 MITRATECH 301 12.2.18 NAVEX GLOBAL 302 12.2.19 CROWE 303 12.2.20 METRICSTREAM 304 12.2.21 IMANAGE 305 12.2.22 UPGUARD 306 12.3 STARTUPS/SMES 307 12.3.1 ROBUST INTELLIGENCE 307 12.3.2 YIELDS.IO 308 12.3.3 SCRUT AUTOMATION 309 12.3.4 DATATRON 310 12.3.5 KRISTA 311 12.3.6 FAIRLY AI 312 12.3.7 MODELOP 313 12.3.8 ARMILLA AI 314 12.3.9 VALIDMIND 315 13 ADJACENT AND RELATED MARKETS 316 13.1 INTRODUCTION 316 13.2 GENERATIVE AI MARKET - GLOBAL FORECAST TO 2030 316 13.2.1 MARKET DEFINITION 316 13.2.2 MARKET OVERVIEW 316 13.2.2.1 Generative AI market, by offering 316 13.2.2.2 Generative AI market, by data modality 317 13.2.2.3 Generative AI market, by application 318 13.2.2.4 Generative AI market, by vertical 319 13.2.2.5 Generative AI market, by region 320 13.3 MLOPS MARKET - GLOBAL FORECAST TO 2027 321 13.3.1 MARKET DEFINITION 321 13.3.2 MARKET OVERVIEW 321 13.3.2.1 MLOps market, by component 321 13.3.2.2 MLOps market, by deployment mode 322 13.3.2.3 MLOps market, by organization size 323 13.3.2.4 MLOps market, by vertical 323 13.3.2.5 MLOps market, by region 325 14 APPENDIX 326 14.1 DISCUSSION GUIDE 326 14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 333 14.3 CUSTOMIZATION OPTIONS 335 14.4 RELATED REPORTS 335 14.5 AUTHOR DETAILS 336
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