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市场调查报告书
商品编码
1776743

2032 年金融风险管理市场人工智慧预测：按组件、风险类型、部署类型、组织规模、技术、应用、最终用户和地区进行全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，预计 2025 年全球金融风险管理人工智慧市场规模将达到 202 亿美元，到 2032 年将达到 922 亿美元，预测期内复合年增长率为 24.2%。

金融风险管理中的人工智慧运用先进的演算法和机器学习来检测、评估和降低信贷、市场和业务领域的风险。它帮助金融机构发现诈欺行为、预测违约、优化交易策略并确保合规。透过即时分析大量数据，人工智慧可以改善决策，提高准确性，并支援更快、更聪明地应对不断变化的财务威胁。

根据英格兰银行和金融行为监理局的《2024年英国金融服务人工智慧报告》，截至2024年底，75%接受调查的金融公司将已经在使用人工智慧技术。

加强监管审查和合规要求

全球金融体係日益增长的监管预期是推动人工智慧在风险管理领域应用的主要驱动力。金融机构如今面临巴塞尔协议III和洗钱防制法规等框架下的严格合规要求，这些要求要求即时监控和准确报告。人工智慧系统可以自动化合规工作流程，产生审核报告，主动预防潜在违规行为，并跟上不断变化的监管情况。此功能有助于减轻人工监管负担，同时确保遵守复杂的合规标准，使人工智慧成为维护业务诚信和避免惩罚性罚款的关键。

实施成本高且人员短缺

人工智慧基础设施的巨额前期投资是其应用的重大障碍。企业必须将资源分配给先进的运算硬体、资料管理系统和持续的维护。此外，能够设计和管理人工智慧风险模型的熟练专业人员的短缺，导致人才市场竞争日益激烈，人事费用不断上升。与旧有系统的整合挑战通常需要昂贵的客製化和更长的实施时间。此外，培训员工使用人工智慧工具增加了营运的复杂性，持续的模型更新和合规性监控也给预算带来了压力，这对财务灵活性较低的小型金融机构尤其严重。

加强诈欺侦测和预防

人工智慧透过即时分析来自不同资料来源的交易模式、行为异常和风险指标，彻底改变了诈欺预防方式。机器学习演算法能够侦测出规避传统规则系统的各种进阶诈骗方案，包括合成身分诈骗等新兴威胁。该技术能够同时处理数百万笔交易，并以高精度识别可疑活动，同时最大限度地减少误报。人工智慧系统能够持续学习新的诈骗模式，并动态适应不断发展的犯罪技术。这种积极主动的方法可以保护金融机构免受直接财务损失，维护客户信任，并加强监管合规性，从而为人工智慧投资创造丰厚的投资回报。

集中度风险和对第三方的依赖

过度依赖少数人工智慧提供者会造成系统性漏洞。金融机构之间的共用依赖关係可能会放大服务中断和模型偏差所带来的风险。人工智慧专业知识集中在大型科技公司，引发了人们对资料安全、智慧财产权风险和营运独立性的担忧。许多人工智慧系统的「黑箱」特性使合规审核变得复杂，因为机构难以解读决策流程。第三方供应商的风险包括服务中断、平台产品的策略转变以及潜在的锁定效应，所有这些都可能同时扰乱多家金融机构的风险管理业务。

COVID-19的影响：

新冠疫情加速了人工智慧在金融风险管理的应用，金融机构也因此经历了前所未有的市场波动。金融机构利用人工智慧模型分析即时经济数据，在不确定的市场条件下评估信用风险，并在转向远距业务的过程中保持业务连续性。传统的风险管理工具已被证明不足以应对这些挑战，因此加大了对人工智慧预测分析和压力测试的投资。然而，经济萎缩限制了技术预算，迫使金融机构优先考虑关键实施，同时推迟了全面的系统改革。

预计大型企业板块在预测期内将占据最大份额

由于大型企业拥有复杂的营运需求和雄厚的资源实力，预计将在预测期内占据最大的市场占有率。为了满足监管要求并管理多样化的风险敞口，这些企业正在投资全面的人工智慧解决方案，包括先进的运算基础设施和专业的人才招募。高交易量为人工智慧支援的诈欺检测、信用评分和市场风险分析创造了理想的使用案例。虽然规模可以透过提高业务效率和降低风险来带来可观的投资回报，但监管合规性要求正在推动对自动化监控系统的需求。

预计金融科技领域在预测期内将以最高复合年增长率成长

预计金融科技公司细分市场将在预测期内呈现最高成长率。其数位原民架构能够快速部署用于信用评分、诈欺预防和合规的人工智慧工具，而不受旧有系统的限制。创业投资资金筹措和监管沙盒支援尖端应用的实验，以客户为中心的经营模式鼓励对即时风险评估和个人化服务的投资。云端基础设施促进了可扩展的实施，满足尚未开发的市场需求并提供创新的金融产品，使这些公司能够实现持续的高成长。

占比最高的地区：

在预测期内，由于技术创新和健全的法规结构，北美预计将占据最大的市场占有率。摩根大通等领先的金融机构正在开发人工智慧风险管理应用，而领先的技术供应商和研究机构则正在培育协作生态系统。清晰的监管准则正在推动人工智慧的普及，而成熟的资本市场则推动对先进风险管理工具的需求。强大的公司管治标准和对金融科技解决方案的投资正在进一步巩固该地区的主导地位。

复合年增长率最高的地区：

预计亚太地区在预测期内的复合年增长率最高。不断壮大的中产阶级和智慧型手机的广泛使用，正在催生对人工智慧金融服务的需求。中国和印度等国家正大力投资人工智慧研究，推动金融应用的创新。多元化的法规环境使得人工智慧解决方案的试验在保持监督的同时得以进行。数位支付和网路银行平台的快速普及，正在推动对进阶诈欺侦测和风险管理能力的需求，为人工智慧提供者创造庞大的商机。

免费客製化服务：

此报告的订阅者可以从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他市场公司进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球金融风险管理人工智慧市场（按组成部分）

  • 解决方案（软体）
    • 风险评估和评分软体
    • 诈欺检测与预防系统
    • 信用风险管理工具
    • 演算法交易风险工具
    • 监理与合规风险软体
  • 服务
    • 咨询服务
    • 实施和整合服务
    • 支援和维护服务
    • 託管服务

6. 全球金融风险管理人工智慧市场（按风险类型）

  • 信用风险
  • 市场风险
  • 操作风险
  • 流动性风险
  • 模型风险

7. 全球金融风险管理人工智慧市场（依实施类型）

  • 本地
  • 云端基础

8. 全球金融风险管理人工智慧市场（依组织规模）

  • 大公司
  • 小型企业

9. 全球金融风险管理人工智慧市场（按技术）

  • 机器学习（ML）
    • 深度学习
    • 强化学习
  • 自然语言处理（NLP）
  • 电脑视觉

第十章 全球金融风险管理人工智慧市场（按应用）

  • 诈欺侦测和风险缓解
  • 监理合规监控与报告
  • 信用风险评估
  • 市场预测与分析
  • 营运效率和自动化

第 11 章。全球金融风险管理 AI 市场（按最终用户）

  • 银行
  • 保险公司
  • 资产管理公司
  • 信用合作社
  • 金融科技公司
  • 避险基金
  • NBFC（非银行金融公司）
  • 监管机构

12. 全球金融风险管理人工智慧市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十三章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十四章 公司概况

  • 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.