세계의 AI 모델 리스크 관리 시장 : 시장 규모, 점유율, 성장 분석 - 제공별, 리스크 유형별, 용도별, 산업별, 지역별 - 예측(-2029년)
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
상품코드:1515622
리서치사:MarketsandMarkets
발행일:2024년 07월
페이지 정보:영문 337 Pages
라이선스 & 가격 (부가세 별도)
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한글목차
AI 모델 리스크 관리 시장 규모는 2024년 57억 달러에서 2029년 105억 달러로 성장해 예측 기간 동안 12.9%의 CAGR을 기록할 것으로 예상됩니다.
이 시장은 강력한 보안 프로토콜 구축, 컴플라이언스 모니터링, 새로운 위협에 대한 효과적인 대응, 수작업으로 인한 오류에 대한 위험 평가 자동화, 모델 라이프사이클 자동화, 효율성 향상, 최종 생산 모델의 품질 향상에 대한 요구가 증가함에 따라 성장할 것으로 예상됩니다.
조사 범위
조사 대상 연도
2019-2029년
기준 연도
2023년
예측 기간
2024-2029년
검토 단위
달러(10억 달러)
부문
제공별, 리스크 유형별, 용도별, 산업별, 지역별
대상 지역
북미, 아시아태평양, 유럽, 중동 및 아프리카, 라틴아메리카
설명 가능한 AI 부문은 AI 모델 리스크 관리 분야에서 빠르게 성장하고 있습니다. 이러한 성장의 배경에는 AI를 활용한 의사결정 과정의 투명성과 신뢰성에 대한 요구가 증가하고 있기 때문입니다. 다양한 산업 분야의 조직이 AI 시스템을 업무에 통합하는 가운데, 설명 가능한 AI(XAI)는 AI 모델이 어떻게 의사결정을 내리는지에 대한 인사이트를 제공하여 이해관계자가 잠재적인 장애물과 오류를 식별할 수 있도록 돕습니다. 정부 및 규제 기관은 조직이 공정성, 책임성, 투명성을 입증해야 한다는 엄격한 가이드라인을 제정하고 있습니다. 또한, 산업계의 AI 도입은 AI 모델의 복잡성에 대응할 수 있는 효과적인 리스크 관리 전략의 필요성을 불러일으켰습니다. 이는 AI 모델의 전반적인 성능을 향상시킬 뿐만 아니라 AI 기반 의사결정 프로세스에 대한 이해관계자의 신뢰와 믿음을 높일 수 있습니다.
아시아태평양은 첨단 기술 도입 증가와 금융 서비스 확대 등 여러 요인으로 인해 예측 기간 동안 가장 높은 성장률을 보일 것으로 예상됩니다. 아시아태평양 경제가 빠르게 성장함에 따라 모델 리스크 관리와 같은 효과적인 리스크 관리 시스템이 도입되고 있습니다. 인프라 및 디지털 업그레이드에 대한 투자도 고급 리스크 분석 및 컴플라이언스 툴에 대한 수요를 가속화하고 있습니다. 아시아태평양 기업들은 시장 변화에 따라 경쟁력을 유지하고 규제에 대응하기 위해 노력하고 있으며, 이에 따라 철저한 AI 모델 리스크 관리 소프트웨어에 대한 수요가 증가하고 있습니다.
이 보고서는 세계 AI 모델 리스크 관리 시장을 조사하여 제공별, 리스크 유형별, 용도별, 산업별, 지역별 동향, 시장 진입 기업 개요 등을 정리한 보고서입니다.
목차
제1장 소개
제2장 조사 방법
제3장 주요 요약
제4장 주요 인사이트
제5장 시장 개요와 업계 동향
소개
시장 역학
AI 모델 리스크 관리 시장의 진화
공급망 분석
생태계 분석
사례 연구 분석
기술 분석
주요 회의와 이벤트(2024-2025년)
투자 상황과 자금 조달 시나리오
규제 상황
특허 분석
가격 분석
Porter's Five Forces 분석
고객 비즈니스에 영향을 미치는 동향/혼란
주요 이해관계자와 구입 기준
제6장 AI 모델 리스크 관리 시장, 제공별
소개
소프트웨어
전개 방식
서비스
제7장 AI 모델 리스크 관리 시장, 리스크 유형별
소개
보안 리스크
윤리적 리스크
운영 리스크
제8장 AI 모델 리스크 관리 시장, 용도별
소개
감정 분석
사기 탐지와 리스크 경감
모델 재고 관리
데이터 분류와 라벨링
규제 준수 감시
고객 세분화와 타겟팅
기타
제9장 AI 모델 리스크 관리 시장, 업계별
소개
BFSI
소매·E-Commerce
통신
제조
헬스케어·생명과학
미디어·엔터테인먼트
IT·ITES
정부·공공부문
기타
제10장 AI 모델 리스크 관리 시장, 지역별
소개
북미
유럽
아시아태평양
중동 및 아프리카
라틴아메리카
제11장 경쟁 상황
개요
주요 진출 기업 전략/비책
매출 분석
시장 점유율 분석
제품 비교 분석
주요 벤더의 기업 평가와 재무 지표
기업 평가 매트릭스 : 주요 진출 기업, 2023년
기업 평가 매트릭스 : 스타트업/중소기업, 2023년
경쟁 시나리오와 동향
제12장 기업 개요
소개
주요 진출 기업
MICROSOFT
IBM
SAS INSTITUTE
AWS
GOOGLE
H2O.AI
LOGICGATE
LOGICMANAGER
C3 AI
MATHWORKS
ALTERYX
AUDITBOARD
DATABRICKS
APPARITY
CIMCON SOFTWARE
EMPOWERED SYSTEMS
MITRATECH
NAVEX GLOBAL
CROWE
METRICSTREAM
IMANAGE
UPGUARD
스타트업/중소기업
ROBUST INTELLIGENCE
YIELDS.IO
SCRUT AUTOMATION
DATATRON
KRISTA
FAIRLY AI
MODELOP
ARMILLA AI
VALIDMIND
제13장 인접 시장과 관련 시장
제14장 부록
ksm
영문 목차
영문목차
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.
Scope of the Report
Years Considered for the Study
2019-2029
Base Year
2023
Forecast Period
2024-2029
Units Considered
USD (Billion)
Segments
Offering, Risk Type, Application, Vertical, and Region
Regions covered
North America, Asia Pacific, Europe, Middle East & Africa, and Latin America
"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 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 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
