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市場調查報告書
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1776743

2032 年金融風險管理市場人工智慧預測：按組件、風險類型、部署類型、組織規模、技術、應用、最終用戶和地區進行全球分析

AI in Financial Risk Management Market Forecasts to 2032 - Global Analysis by Component (Solutions and Services), Risk Type, Deployment Mode, Organization Size, Technology, Application, End User and Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計 2025 年全球金融風險管理人工智慧市場規模將達到 202 億美元，到 2032 年將達到 922 億美元，預測期內複合年成長率為 24.2%。

金融風險管理中的人工智慧運用先進的演算法和機器學習來檢測、評估和降低信貸、市場和業務領域的風險。它幫助金融機構發現詐欺行為、預測違約、最佳化交易策略並確保合規。透過即時分析大量數據，人工智慧可以改善決策，提高準確性，並支援更快、更聰明地應對不斷變化的財務威脅。

根據英格蘭銀行和金融行為監理局的《2024年英國金融服務人工智慧報告》，截至2024年底，75%接受調查的金融公司將已經在使用人工智慧技術。

加強監管審查和合規要求

全球金融體係日益成長的監管預期是推動人工智慧在風險管理領域應用的主要驅動力。金融機構如今面臨巴塞爾協議III和洗錢防制法規等框架下的嚴格合規要求，這些要求要求即時監控和準確報告。人工智慧系統可以自動化合規工作流程，產生審核報告，主動預防潛在違規行為，並跟上不斷變化的監管情況。此功能有助於減輕人工監管負擔，同時確保遵守複雜的合規標準，使人工智慧成為維護業務誠信和避免懲罰性罰款的關鍵。

實施成本高且人員短缺

人工智慧基礎設施的巨額前期投資是其應用的重大障礙。企業必須將資源分配給先進的運算硬體、資料管理系統和持續的維護。此外，能夠設計和管理人工智慧風險模型的熟練專業人員的短缺，導致人才市場競爭日益激烈，人事費用不斷上升。與舊有系統的整合挑戰通常需要昂貴的客製化和更長的實施時間。此外，培訓員工使用人工智慧工具增加了營運的複雜性，持續的模型更新和合規性監控也給預算帶來了壓力，這對財務靈活性較低的小型金融機構尤其嚴重。

加強詐欺偵測和預防

人工智慧透過即時分析來自不同資料來源的交易模式、行為異常和風險指標，徹底改變了詐欺預防方式。機器學習演算法能夠偵測出規避傳統規則系統的各種進階詐騙方案，包括合成身分詐騙等新興威脅。該技術能夠同時處理數百萬筆交易，並以高精度識別可疑活動，同時最大限度地減少誤報。人工智慧系統能夠持續學習新的詐騙模式，並動態適應不斷發展的犯罪技術。這種積極主動的方法可以保護金融機構免受直接財務損失，維護客戶信任，並加強監管合規性，從而為人工智慧投資創造豐厚的投資回報。

集中度風險和對第三方的依賴

過度依賴少數人工智慧提供者會造成系統性漏洞。金融機構之間的共用依賴關係可能會放大服務中斷和模型偏差所帶來的風險。人工智慧專業知識集中在大型科技公司，引發了人們對資料安全、智慧財產權風險和營運獨立性的擔憂。許多人工智慧系統的「黑箱」特性使合規審核變得複雜，因為機構難以解讀決策流程。第三方供應商的風險包括服務中斷、平台產品的策略轉變以及潛在的鎖定效應，所有這些都可能同時擾亂多家金融機構的風險管理業務。

COVID-19的影響：

新冠疫情加速了人工智慧在金融風險管理的應用，金融機構也因此經歷了前所未有的市場波動。金融機構利用人工智慧模型分析即時經濟數據，在不確定的市場條件下評估信用風險，並在轉向遠距業務的過程中保持業務連續性。傳統的風險管理工具已被證明不足以應對這些挑戰，因此加大了對人工智慧預測分析和壓力測試的投資。然而，經濟萎縮限制了技術預算，迫使金融機構優先考慮關鍵實施，同時推遲了全面的系統改革。

預計大型企業板塊在預測期內將佔據最大佔有率

由於大型企業擁有複雜的營運需求和雄厚的資源實力，預計將在預測期內佔據最大的市場佔有率。為了滿足監管要求並管理多樣化的風險敞口，這些企業正在投資全面的人工智慧解決方案，包括先進的運算基礎設施和專業的人才招募。高交易量為人工智慧支援的詐欺檢測、信用評分和市場風險分析創造了理想的使用案例。雖然規模可以透過提高業務效率和降低風險來帶來可觀的投資回報，但監管合規性要求正在推動對自動化監控系統的需求。

預計金融科技領域在預測期內將以最高複合年成長率成長

預計金融科技公司細分市場將在預測期內呈現最高成長率。其數位原民架構能夠快速部署用於信用評分、詐欺預防和合規的人工智慧工具，而不受舊有系統的限制。創業投資資金籌措和監管沙盒支援尖端應用的實驗，以客戶為中心的經營模式鼓勵對即時風險評估和個人化服務的投資。雲端基礎設施促進了可擴展的實施，滿足尚未開發的市場需求並提供創新的金融產品，使這些公司能夠實現持續的高成長。

佔比最高的地區：

在預測期內，由於技術創新和健全的法規結構，北美預計將佔據最大的市場佔有率。摩根大通等領先的金融機構正在開發人工智慧風險管理應用，而領先的技術供應商和研究機構則正在培育協作生態系統。清晰的監管準則正在推動人工智慧的普及，而成熟的資本市場則推動對先進風險管理工具的需求。強大的公司管治標準和對金融科技解決方案的投資正在進一步鞏固該地區的主導地位。

複合年成長率最高的地區：

預計亞太地區在預測期內的複合年成長率最高。不斷壯大的中產階級和智慧型手機的廣泛使用，正在催生對人工智慧金融服務的需求。中國和印度等國家正大力投資人工智慧研究，推動金融應用的創新。多元化的法規環境使得人工智慧解決方案的試驗在保持監督的同時得以進行。數位支付和網路銀行平台的快速普及，正在推動對進階詐欺偵測和風險管理能力的需求，為人工智慧提供者創造龐大的商機。

免費客製化服務：

此報告的訂閱者可以從以下免費自訂選項中選擇一項：

  • 公司簡介
    • 對最多三家其他市場公司進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 地理細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球金融風險管理人工智慧市場（按組成部分）

  • 解決方案（軟體）
    • 風險評估和評分軟體
    • 詐欺檢測與預防系統
    • 信用風險管理工具
    • 演算法交易風險工具
    • 監理與合規風險軟體
  • 服務
    • 諮詢服務
    • 實施和整合服務
    • 支援和維護服務
    • 託管服務

6. 全球金融風險管理人工智慧市場（按風險類型）

  • 信用風險
  • 市場風險
  • 操作風險
  • 流動性風險
  • 模型風險

7. 全球金融風險管理人工智慧市場（依實施類型）

  • 本地
  • 雲端基礎

8. 全球金融風險管理人工智慧市場（依組織規模）

  • 主要企業
  • 小型企業

9. 全球金融風險管理人工智慧市場（按技術）

  • 機器學習（ML）
    • 深度學習
    • 強化學習
  • 自然語言處理（NLP）
  • 電腦視覺

第10章 全球金融風險管理人工智慧市場（按應用）

  • 詐欺偵測和風險緩解
  • 監理合規監控與報告
  • 信用風險評估
  • 市場預測與分析
  • 營運效率和自動化

第 11 章。全球金融風險管理 AI 市場（按最終用戶）

  • 銀行
  • 保險公司
  • 資產管理公司
  • 信用合作社
  • 金融科技公司
  • 避險基金
  • NBFC（非銀行金融公司）
  • 監管機構

12. 全球金融風險管理人工智慧市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第13章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第14章 公司概況

  • 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.
  • Upstart Holdings, Inc.
Product Code: SMRC30074

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.

Market Dynamics:

Driver:

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.

Restraint:

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.

Opportunity:

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.

Threat:

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.

Covid-19 Impact:

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.

Region with largest share:

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.

Region with highest CAGR:

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.

Key Developments:

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.

Components Covered:

  • Solutions (Software)
  • Services

Risk Types Covered:

  • Credit Risk
  • Market Risk
  • Operational Risk
  • Liquidity Risk
  • Model Risk

Deployment Modes Covered:

  • On-Premise
  • Cloud-Based

Organization Sizes Covered:

  • Large Enterprises
  • Small & Medium Enterprises (SMEs)

Technologies Covered:

  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Computer Vision

Applications Covered:

  • Fraud Detection & Risk Reduction
  • Regulatory Compliance Monitoring & Reporting
  • Credit Risk Assessment
  • Market Prediction & Analysis
  • Operational Efficiency & Automation

End Users Covered:

  • Banks
  • Insurance Companies
  • Asset Management Firms
  • Credit Unions
  • FinTech Companies
  • Hedge Funds
  • NBFCs (Non-Banking Financial Companies)
  • Regulatory Bodies

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Financial Risk Management Market, By Component

  • 5.1 Introduction
  • 5.2 Solutions (Software)
    • 5.2.1 Risk Assessment & Scoring Software
    • 5.2.2 Fraud Detection & Prevention Systems
    • 5.2.3 Credit Risk Management Tools
    • 5.2.4 Algorithmic Trading Risk Tools
    • 5.2.5 Regulatory & Compliance Risk Software
  • 5.3 Services
    • 5.3.1 Consulting Services
    • 5.3.2 Implementation & Integration Services
    • 5.3.3 Support & Maintenance Services
    • 5.3.4 Managed Services

6 Global AI in Financial Risk Management Market, By Risk Type

  • 6.1 Introduction
  • 6.2 Credit Risk
  • 6.3 Market Risk
  • 6.4 Operational Risk
  • 6.5 Liquidity Risk
  • 6.6 Model Risk

7 Global AI in Financial Risk Management Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 On-Premise
  • 7.3 Cloud-Based

8 Global AI in Financial Risk Management Market, By Organization Size

  • 8.1 Introduction
  • 8.2 Large Enterprises
  • 8.3 Small & Medium Enterprises (SMEs)

9 Global AI in Financial Risk Management Market, By Technology

  • 9.1 Introduction
  • 9.2 Machine Learning (ML)
    • 9.2.1 Deep Learning
    • 9.2.2 Reinforcement Learning
  • 9.3 Natural Language Processing (NLP)
  • 9.4 Computer Vision

10 Global AI in Financial Risk Management Market, By Application

  • 10.1 Introduction
  • 10.2 Fraud Detection & Risk Reduction
  • 10.3 Regulatory Compliance Monitoring & Reporting
  • 10.4 Credit Risk Assessment
  • 10.5 Market Prediction & Analysis
  • 10.6 Operational Efficiency & Automation

11 Global AI in Financial Risk Management Market, By End User

  • 11.1 Introduction
  • 11.2 Banks
  • 11.3 Insurance Companies
  • 11.4 Asset Management Firms
  • 11.5 Credit Unions
  • 11.6 FinTech Companies
  • 11.7 Hedge Funds
  • 11.8 NBFCs (Non-Banking Financial Companies)
  • 11.9 Regulatory Bodies

12 Global AI in Financial Risk Management Market, By Geography

  • 12.1 Introduction
  • 12.2 North America
    • 12.2.1 US
    • 12.2.2 Canada
    • 12.2.3 Mexico
  • 12.3 Europe
    • 12.3.1 Germany
    • 12.3.2 UK
    • 12.3.3 Italy
    • 12.3.4 France
    • 12.3.5 Spain
    • 12.3.6 Rest of Europe
  • 12.4 Asia Pacific
    • 12.4.1 Japan
    • 12.4.2 China
    • 12.4.3 India
    • 12.4.4 Australia
    • 12.4.5 New Zealand
    • 12.4.6 South Korea
    • 12.4.7 Rest of Asia Pacific
  • 12.5 South America
    • 12.5.1 Argentina
    • 12.5.2 Brazil
    • 12.5.3 Chile
    • 12.5.4 Rest of South America
  • 12.6 Middle East & Africa
    • 12.6.1 Saudi Arabia
    • 12.6.2 UAE
    • 12.6.3 Qatar
    • 12.6.4 South Africa
    • 12.6.5 Rest of Middle East & Africa

13 Key Developments

  • 13.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 13.2 Acquisitions & Mergers
  • 13.3 New Product Launch
  • 13.4 Expansions
  • 13.5 Other Key Strategies

14 Company Profiling

  • 14.1 International Business Machines Corporation (IBM)
  • 14.2 Microsoft Corporation
  • 14.3 Google LLC (Alphabet Inc.)
  • 14.4 Amazon Web Services, Inc.
  • 14.5 Oracle Corporation
  • 14.6 SAS Institute Inc.
  • 14.7 FICO (Fair Isaac Corporation)
  • 14.8 Moody's Analytics, Inc.
  • 14.9 S&P Global Inc.
  • 14.10 Palantir Technologies Inc.
  • 14.11 Deloitte Touche Tohmatsu Limited
  • 14.12 KPMG International Limited
  • 14.13 PwC (PricewaterhouseCoopers International Limited)
  • 14.14 Accenture plc
  • 14.15 Zest AI, Inc.
  • 14.16 Ayasdi AI LLC
  • 14.17 Riskified Ltd.
  • 14.18 Upstart Holdings, Inc.

List of Tables

  • Table 1 Global AI in Financial Risk Management Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Financial Risk Management Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI in Financial Risk Management Market Outlook, By Solutions (Software) (2024-2032) ($MN)
  • Table 4 Global AI in Financial Risk Management Market Outlook, By Risk Assessment & Scoring Software (2024-2032) ($MN)
  • Table 5 Global AI in Financial Risk Management Market Outlook, By Fraud Detection & Prevention Systems (2024-2032) ($MN)
  • Table 6 Global AI in Financial Risk Management Market Outlook, By Credit Risk Management Tools (2024-2032) ($MN)
  • Table 7 Global AI in Financial Risk Management Market Outlook, By Algorithmic Trading Risk Tools (2024-2032) ($MN)
  • Table 8 Global AI in Financial Risk Management Market Outlook, By Regulatory & Compliance Risk Software (2024-2032) ($MN)
  • Table 9 Global AI in Financial Risk Management Market Outlook, By Services (2024-2032) ($MN)
  • Table 10 Global AI in Financial Risk Management Market Outlook, By Consulting Services (2024-2032) ($MN)
  • Table 11 Global AI in Financial Risk Management Market Outlook, By Implementation & Integration Services (2024-2032) ($MN)
  • Table 12 Global AI in Financial Risk Management Market Outlook, By Support & Maintenance Services (2024-2032) ($MN)
  • Table 13 Global AI in Financial Risk Management Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 14 Global AI in Financial Risk Management Market Outlook, By Risk Type (2024-2032) ($MN)
  • Table 15 Global AI in Financial Risk Management Market Outlook, By Credit Risk (2024-2032) ($MN)
  • Table 16 Global AI in Financial Risk Management Market Outlook, By Market Risk (2024-2032) ($MN)
  • Table 17 Global AI in Financial Risk Management Market Outlook, By Operational Risk (2024-2032) ($MN)
  • Table 18 Global AI in Financial Risk Management Market Outlook, By Liquidity Risk (2024-2032) ($MN)
  • Table 19 Global AI in Financial Risk Management Market Outlook, By Model Risk (2024-2032) ($MN)
  • Table 20 Global AI in Financial Risk Management Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 21 Global AI in Financial Risk Management Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 22 Global AI in Financial Risk Management Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 23 Global AI in Financial Risk Management Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 24 Global AI in Financial Risk Management Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 25 Global AI in Financial Risk Management Market Outlook, By Small & Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 26 Global AI in Financial Risk Management Market Outlook, By Technology (2024-2032) ($MN)
  • Table 27 Global AI in Financial Risk Management Market Outlook, By Machine Learning (ML) (2024-2032) ($MN)
  • Table 28 Global AI in Financial Risk Management Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 29 Global AI in Financial Risk Management Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 30 Global AI in Financial Risk Management Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 31 Global AI in Financial Risk Management Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 32 Global AI in Financial Risk Management Market Outlook, By Application (2024-2032) ($MN)
  • Table 33 Global AI in Financial Risk Management Market Outlook, By Fraud Detection & Risk Reduction (2024-2032) ($MN)
  • Table 34 Global AI in Financial Risk Management Market Outlook, By Regulatory Compliance Monitoring & Reporting (2024-2032) ($MN)
  • Table 35 Global AI in Financial Risk Management Market Outlook, By Credit Risk Assessment (2024-2032) ($MN)
  • Table 36 Global AI in Financial Risk Management Market Outlook, By Market Prediction & Analysis (2024-2032) ($MN)
  • Table 37 Global AI in Financial Risk Management Market Outlook, By Operational Efficiency & Automation (2024-2032) ($MN)
  • Table 38 Global AI in Financial Risk Management Market Outlook, By End User (2024-2032) ($MN)
  • Table 39 Global AI in Financial Risk Management Market Outlook, By Banks (2024-2032) ($MN)
  • Table 40 Global AI in Financial Risk Management Market Outlook, By Insurance Companies (2024-2032) ($MN)
  • Table 41 Global AI in Financial Risk Management Market Outlook, By Asset Management Firms (2024-2032) ($MN)
  • Table 42 Global AI in Financial Risk Management Market Outlook, By Credit Unions (2024-2032) ($MN)
  • Table 43 Global AI in Financial Risk Management Market Outlook, By FinTech Companies (2024-2032) ($MN)
  • Table 44 Global AI in Financial Risk Management Market Outlook, By Hedge Funds (2024-2032) ($MN)
  • Table 45 Global AI in Financial Risk Management Market Outlook, By NBFCs (Non-Banking Financial Companies) (2024-2032) ($MN)
  • Table 46 Global AI in Financial Risk Management Market Outlook, By Regulatory Bodies (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.