Stratistics MRC에 따르면 세계의 금융 리스크 관리용 AI 시장은 2025년 202억 달러를 차지하고 예측 기간 동안 CAGR 24.2%로 성장해 2032년까지 922억 달러에 이를 것으로 예측됩니다.
금융 리스크 관리용 AI는 고급 알고리즘과 머신러닝을 사용하여 신용, 시장 및 업무 분야에 걸친 위험을 감지, 평가 및 완화합니다. 대량의 데이터를 실시간으로 분석함으로써 AI는 의사결정을 개선하고 정확성을 높이고 진화하는 금융 위협에 대한 신속하고 스마트한 대응을 지원합니다.
영국은행과 금융행동 감시기구에 의한 '영국금융서비스의 인공지능 2024년 보고서'에 따르면 2024년 후반 시점에서 조사 대상이 된 금융회사의 75%가 이미 AI 기술을 이용하고 있습니다.
규제 당국 모니터링 및 컴플라이언스 요구 증가
세계 금융 시스템 전반에 걸친 규제에 대한 기대는 리스크 관리에서 AI 도입의 주요 촉진요인이 되고 있습니다. AI 시스템은 컴플라이언스 워크플로우를 자동화하고, 감사 대응 보고서 작성, 잠재적인 위반의 미연 방지, 진화하는 규제 상황에 대응을 가능하게 합니다.
높은 도입 비용과 인력 부족
AI 인프라에 대한 많은 투자는 도입에 큰 장벽을 가져다 줍니다. 레거시 시스템과의 통합 과제는 비용이 많이 드는 커스터마이징과 도입 기간의 연장이 필요합니다.
부정 검지 및 방지 강화
AI는 거래 패턴, 행동 이상, 이종 데이터 소스의 리스크 지표를 실시간으로 분석하여 부정 방지를 변화시킵니다. AI 시스템은 새로운 사기 패턴에서 지속적으로 학습하고 진화하는 범죄 수법에 동적으로 적응할 수 있습니다.
집중 위험과 제3자 의존
제한된 수의 AI 제공업체에 과도하게 의존하면 시스템 취약성이 발생합니다. 타사 공급업체의 위험은 서비스 중단, 플랫폼 제공의 전략적 전환, 잠재적인 잠금 효과 등을 포함하여 이들 모두가 여러 금융기관의 위험 관리 업무를 동시에 혼란시킬 수 있습니다.
COVID-19의 팬데믹은 금융기관이 미증유의 변동성을 극복하면서 금융 리스크 관리용 AI의 채용을 가속화하였습니다. 이러한 문제에 대해 기존의 리스크 관리 툴로는 불충분한 것으로 판명되었기 때문에 AI를 활용한 예측 분석이나 스트레스 테스트에 대한 투자가 증가했습니다.
예측 기간 동안 대기업 부문이 최대가 될 전망
대기업 부문은 복잡한 운영 요구와 풍부한 자원 역량으로 인해 예측 기간 동안 최대 시장 점유율을 차지할 것으로 예측됩니다. 거래량이 많기 때문에 AI를 활용한 부정 감지, 신용 평가, 시장 리스크 분석에 이상적인 이용 사례가 탄생합니다.
예측 기간 동안 Fintech 기업 부문의 CAGR이 가장 높을 것으로 예상
예측 기간 동안 핀테크 기업 부문이 가장 높은 성장률을 보일 것으로 예측됩니다. 첨단 용도 실험을 지원하고 고객 중심 비즈니스 모델은 실시간 위험 평가와 개인화된 서비스에 대한 투자를 촉진합니다.
예측기간 동안 기술 혁신과 견고한 규제 프레임워크를 통해 북미가 최대 시장 점유율을 차지할 것으로 예측됩니다. 명확한 규제 지침이 AI 채용을 뒷받침하고 성숙한 자본 시장이 고급 리스크 관리 도구에 대한 수요를 뒷받침하고 있습니다.
예측 기간 중 아시아태평양이 가장 높은 CAGR을 나타낼 것으로 예측됩니다. 다양한 규제 환경이 감독을 유지하면서 AI 솔루션의 실험을 가능하게 합니다.
According to Stratistics MRC, the Global AI in Financial Risk Management Market is accounted for $20.2 billion in 2025 and is expected to reach $92.2 billion by 2032 growing at a CAGR of 24.2% during the forecast period. AI in financial risk management uses advanced algorithms and machine learning to detect, assess, and mitigate risks across credit, market, and operational areas. It helps institutions spot fraud, predict defaults, optimize trading strategies, and ensure regulatory compliance. By analyzing large volumes of data in real time, AI improves decision-making, enhances accuracy, and supports faster, smarter responses to evolving financial threats.
According to the Artificial Intelligence in UK Financial Services 2024 report by the Bank of England and the Financial Conduct Authority, 75% of financial firms surveyed were already using AI technologies as of late 2024.
Increasing regulatory scrutiny and compliance demands
Rising regulatory expectations across global financial systems serve as a key growth driver for AI adoption in risk management. Financial institutions now face stringent compliance requirements under frameworks like Basel III and anti-money laundering regulations, which demand real-time monitoring and precise reporting. AI systems automate compliance workflows, enabling organizations to generate audit-ready reports, flag potential violations proactively, and adapt to evolving regulatory landscapes. This capability reduces manual oversight burdens while ensuring adherence to complex compliance standards, making AI indispensable for maintaining operational integrity and avoiding punitive fines.
High implementation costs and talent shortage
Substantial upfront investments in AI infrastructure pose significant barriers to adoption. Organizations must allocate resources for advanced computing hardware, data management systems, and ongoing maintenance. Additionally, a scarcity of skilled professionals capable of designing and managing AI risk models creates competitive talent markets, driving up labor costs. Legacy system integration challenges often require costly customizations and extended implementation timelines. Training staff to collaborate with AI tools adds operational complexity, while continuous model updates and compliance monitoring strain budgets, particularly impacting smaller institutions with limited financial flexibility.
Enhanced fraud detection and prevention
AI transforms fraud prevention through real-time analysis of transaction patterns, behavioral anomalies, and risk indicators across disparate data sources. Machine learning algorithms detect sophisticated fraud schemes that evade traditional rule-based systems, including emerging threats like synthetic identity fraud. The technology processes millions of transactions simultaneously, identifying suspicious activities with high accuracy while minimizing false positives. AI systems continuously learn from new fraud patterns, enabling dynamic adaptation to evolving criminal tactics. This proactive approach protects institutions from direct financial losses, preserves customer trust, and strengthens regulatory compliance, creating a compelling ROI for AI investments.
Concentration risk and third-party dependence
Overreliance on a limited number of AI providers introduces systemic vulnerabilities. Shared dependencies across institutions can amplify risks during service disruptions or model biases. The concentration of AI expertise in major tech firms raises concerns about data security, intellectual property risks, and operational independence. The "black-box" nature of many AI systems complicates compliance audits, as institutions struggle to interpret decision-making processes. Third-party vendor risks include service interruptions, strategic shifts in platform offerings, and potential lock-in effects, all of which could disrupt risk management operations across multiple institutions simultaneously.
The Covid-19 pandemic accelerated AI adoption in financial risk management as institutions navigated unprecedented volatility. Organizations leveraged AI models to analyze real-time economic data, assess credit risks amid uncertain market conditions, and maintain operational continuity during remote work transitions. Traditional risk management tools proved inadequate against these challenges, prompting increased investment in AI-powered predictive analytics and stress testing. However, economic contractions constrained technology budgets, forcing institutions to prioritize critical implementations while delaying comprehensive system overhauls.
The large enterprises segment is expected to be the largest during the forecast period
The large enterprises segment is expected to account for the largest market share during the forecast period due to their complex operational needs and substantial resource capabilities. These organizations invest in comprehensive AI solutions, including advanced computing infrastructure and specialized talent acquisition, to address regulatory demands and manage diverse risk exposures. Their high transaction volumes create ideal use cases for AI-driven fraud detection, credit assessment, and market risk analysis. Scale enables meaningful ROI through operational efficiency gains and risk mitigation benefits, while regulatory compliance requirements drive demand for automated monitoring systems.
The fintech companies segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the fintech companies segment is predicted to witness the highest growth rate. Their digital-native architectures enable rapid deployment of AI tools for credit scoring, fraud prevention, and compliance without legacy system constraints. Venture capital funding and regulatory sandboxes support experimentation with cutting-edge applications, while customer-centric business models drive investment in real-time risk assessment and personalized services. Cloud infrastructure facilitates scalable implementations, positioning these companies for sustained high growth as they address underserved markets and deliver innovative financial products.
During the forecast period, the North America region is expected to hold the largest market share owing to their technological innovation and robust regulatory frameworks. Major financial institutions like JPMorgan Chase pioneer AI risk management applications, while leading tech providers and research institutions foster a collaborative ecosystem. Clear regulatory guidelines support AI adoption, while mature capital markets drive demand for sophisticated risk management tools. Strong corporate governance standards and investment in fintech solutions further solidify the region's dominant position.
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Expanding middle-class populations and high smartphone adoption create demand for AI-powered financial services. Countries like China and India invest heavily in AI research, fostering innovation in financial applications. Diverse regulatory environments enable experimentation with AI solutions while maintaining oversight. The region's rapid adoption of digital payments and online banking platforms fuels demand for advanced fraud detection and risk management capabilities, creating substantial opportunities for AI providers.
Key players in the market
Some of the key players in AI in Financial Risk Management Market include International Business Machines Corporation (IBM), Microsoft Corporation, Google LLC (Alphabet Inc.), Amazon Web Services, Inc., Oracle Corporation, SAS Institute Inc., FICO (Fair Isaac Corporation), Moody's Analytics, Inc., S&P Global Inc., Palantir Technologies Inc., Deloitte Touche Tohmatsu Limited, KPMG International Limited, PwC (PricewaterhouseCoopers International Limited), Accenture plc, Zest AI, Inc., Ayasdi AI LLC, Riskified Ltd. and Upstart Holdings, Inc.
In May 2025, Palantir Technologies Inc. and TWG Global (TWG) announced a joint venture to redefine AI deployment in banking, investment management, insurance and other financial services. By pairing Palantir's unmatched AI infrastructure with TWG's deep expertise in business operations and financial services, this initiative will enable financial institutions to integrate AI at scale-moving beyond fragmented, piecemeal solutions to a singular, fully embedded, enterprise-wide approach.
In May 2025, IBM released the Agentic AI in Financial Services: Opportunities, Risks, and Responsible Implementation whitepaper, highlighting how autonomous AI systems are poised to revolutionise the financial services sector while emphasising the critical need for responsible implementation and risk management frameworks.
In March 2025, Inait announced collaboration with Microsoft to accelerate the development and commercialization of inait's innovative AI technology, using its unique digital brain AI platform. The collaboration will focus on joint product development, go-to-market strategies, and co-selling initiatives, initially targeting the finance and robotics sectors.