银行业资料分析市场－全球产业规模、份额、趋势、机会及预测（按部署类型、类型、解决方案、最终用户、地区和竞争格局划分，2021-2031年）

全球银行业数据分析市场预计将从2025年的132.9亿美元显着成长至2031年的387.4亿美元，复合年增长率（CAGR）达19.52%。数据分析是对财务记录进行系统性计算检验，它使银行能够识别模式、关联性和趋势，从而指导策略决策。该市场的主要驱动力是迫切需要健全的风险管理框架以及日益增长的个人化客户体验需求，这两方面都要求金融机构快速处理大量交易资讯。此外，严格的监管合规要求迫使金融机构实施严谨的分析措施，以确保透明度并预防金融犯罪，这也是推动数据分析在银行业广泛应用的根本原因。

儘管有这些成长要素，但阻碍市场扩张的一大挑战在于难以将现代分析工具与分散的传统IT基础设施整合，这往往导致资料孤岛和管治问题。随着产业在数据通讯协定规范化方面举步维艰，这种营运脱节现象尤其明显。根据美国银行家协会（ABA）预测，到2024年，71%的银行负责人表示其所在机构缺乏成文的客户资料策略。这种策略规划的缺失阻碍了银行充分利用其数据资产，最终延缓了全球分析市场的整体成熟度。

市场驱动因素

人工智慧 (AI) 和机器学习 (ML) 的整合是推动市场发展的关键因素，使金融机构能够从事后分析转向预测智慧。银行正在利用这些技术处理非结构化资料集，以实现自动信用评分和演算法产品推荐。这项技术转型得益于高普及率：NVIDIA 于 2024 年 2 月发布的《金融服务业人工智慧现状：2024 年趋势》报告显示，91% 的金融服务公司正在评估或实施人工智慧，以增强创新能力和业务连续性。如此广泛的整合需要能够处理复杂模型的强大分析平台，而金融机构透过数据驱动的前瞻性来维持竞争优势的努力，正是推动市场成长的动力。

同时，对即时诈欺侦测日益增长的需求迫使银行部署能够在毫秒内识别异常情况的尖端分析解决方案。随着交易量的增长，传统的基于规则的系统已无法应对不断演变的网路威胁，因此行为分析和模式识别至关重要。这些措施的影响是可以量化的：根据Visa于2024年3月发布的《2024年春季威胁报告》，该银行的分析能力在上年度阻止了价值400亿美元的非法贸易。为了支持这些安全措施和广泛的数位基础设施，大量资金正投入技术升级。摩根大通在2024年拨款约170亿美元用于技术，凸显了以数据为中心的投资的重要性。

市场挑战

市场扩张面临的一大挑战在于，难以将现代分析工具与分散的传统IT基础设施整合，导致资料孤岛大规模，管治漏洞百出。金融机构往往依赖老旧的核心系统，这些系统无法有效率地与新型资料密集型应用对接，几乎不可能聚合​​高级分析所需的整合即时资料集。这种架构上的脱节使得银行无法无缝取得风险建模和个人化客户定位等关键功能所需的交易资讯。因此，无法创建一致的数据环境限制了分析倡议的扩充性，迫使金融机构依赖人工操作且容易出错的流程，从而削弱了现代分析解决方案所承诺的效率和速度。

这种技术壁垒直接阻碍了市场成长，因为它增加了数位转型计划相关的营运风险和成本。在不相容的传统框架上建立高阶分析功能的复杂性导致实施时间过长、成本过高，使得金融机构无法全面进行必要的升级。根据州银行监管机构协会 (CSBS) 预测，到 2024 年，约 80% 的社区银行将把技术采用和成本视为其机构面临的主要内部风险。由于银行为了避免业务中断和财务风险而推迟这些关键的技术升级，全球数据分析的采用进程停滞不前，阻碍了市场充分发挥其潜力。

市场趋势

开放银行和API驱动的资料生态系统的扩展正在从根本上重塑市场，推动金融机构从封闭的专有资料孤岛走向协作互通的网路。这一趋势使得银行能够在获得客户许可的情况下安全地与第三方供应商共用数据，从而促进创新金融产品和简化支付服务的开发，超越了传统的银行介面。商业机构对此生态系统的快速采用也印证了其加速发展。根据万事达卡2024年12月发布的白皮书《开放银行：信任的必要性》，85%的B2B受访者表示目前正在使用开放银行解决方案来提升其财务营运效率。如此高的采用率支持市场向平台模式转型，在这种模式下，数据的流动性将驱动竞争优势。

将生成式人工智慧融入超个人化服务，标誌着银行利用数据方式的重大变革，使其超越静态预测评分，并迈向动态的互动式客户参与。与将使用者粗略分类的传统分析方法不同，生成式模型能够分析个人交易历史和行为的细微差别，从而创造即时、量身定制的金融咨询和自动化的类人互动。随着金融机构逐渐意识到人工智慧对于提升营运效率和客户维繫至关重要，这项技术正蓬勃发展。 NTT DATA 于 2025 年 2 月发布的报告《人工智慧时代的智慧银行》显示，58% 的银行机构已全面拥抱生成式人工智慧的变革潜力。如此广泛的应用凸显了银行业日益重视利用先进演算法，为现代消费者提供他们所期望的客製化、响应式体验。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章：全球银行业数据分析市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 依部署类型（本机部署、云端部署）
  • 依类型（说明分析、诊断性分析、预测性分析、指示性分析）
  • 按解决方案（风险管理（信用风险评估、诈欺侦测与管理、压力测试等）、客户分析、投资组合管理分析、交易分析）
  • 依最终用户（卖家、买家）划分
  • 按地区
  • 按公司（2025 年）
• 市场地图

6. 北美银行业数据分析市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 美国
  • 加拿大
  • 墨西哥

7. 欧洲银行业数据分析市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

8. 亚太地区银行业资料分析市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

9. 中东和非洲银行业资料分析市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美银行业资料分析市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章 全球银行业资料分析市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• International Business Machines Corporation
• SAP SE
• Oracle Corporation
• Microsoft Corporation
• Google LLC
• Amazon Web Services, Inc.
• TIBCO Software, Inc.
• Hewlett Packard Enterprise Co.
• SiSense, Inc.
• Mu Sigma, Inc.
• Dell, Inc.
• Alteryx Inc.
• Teradata Corporation
• Wipro Ltd.
• SAS Institute, Inc.

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Data Analytics in Banking Market is projected to expand significantly, growing from USD 13.29 Billion in 2025 to USD 38.74 Billion by 2031, achieving a CAGR of 19.52%. Defined as the systematic computational examination of financial records, data analytics allows banks to identify patterns, correlations, and trends that guide strategic decision-making. The market is primarily fueled by the urgent necessity for robust risk management frameworks and the rising demand for personalized customer experiences, both of which require institutions to process massive volumes of transactional information rapidly. Furthermore, strict regulatory compliance mandates force financial institutions to implement precise analytical measures to ensure transparency and prevent financial crimes, serving as a fundamental catalyst for widespread industry adoption.

Despite these growth drivers, a major challenge impeding market expansion is the difficulty of merging modern analytical tools with fragmented legacy IT infrastructures, often resulting in data silos and governance issues. This operational disconnect is highlighted by the industry's struggle to formalize data protocols; according to the American Bankers Association, in 2024, 71 percent of bank marketers reported that their institutions lacked a written or documented customer data strategy. Such gaps in strategic planning prevent banks from fully utilizing their data assets, thereby slowing the overall maturity of the global analytics market.

Market Driver

The integration of Artificial Intelligence (AI) and Machine Learning (ML) serves as a primary engine for the market, empowering institutions to shift from retrospective analysis to predictive intelligence. Banks leverage these technologies to process unstructured datasets, facilitating automated credit scoring and algorithmic product recommendations. This technological shift is evidenced by high adoption rates; according to NVIDIA's 'State of AI in Financial Services: 2024 Trends' report from February 2024, 91 percent of financial services companies were assessing or using AI to drive innovation and operational resilience. Such widespread integration necessitates robust analytics platforms capable of handling complex models, fueling market growth as financial entities strive to maintain competitive advantages through data-driven foresight.

Simultaneously, the escalating demand for real-time fraud detection compels banks to deploy modern analytical solutions capable of identifying anomalies within milliseconds. As transaction volumes rise, traditional rule-based systems are proving inadequate against evolving cyber threats, necessitating the use of behavioral analytics and pattern recognition. The effectiveness of these measures is quantifiable; according to Visa's 'Spring 2024 Threats Report' from March 2024, the company's analytics capabilities helped block $40 billion in fraudulent activity during the previous year. To support these security measures and broader digital infrastructure, massive capital is being directed toward technological fortification, with JPMorgan Chase allocating approximately $17 billion to technology in 2024, underscoring the critical role of data-centric investment.

Market Challenge

A significant challenge impeding market expansion is the difficulty of integrating modern analytical tools with fragmented legacy IT infrastructure, which creates substantial data silos and governance voids. Financial institutions often rely on aged core systems that cannot efficiently communicate with newer, data-intensive applications, making it nearly impossible to aggregate the real-time, unified datasets required for advanced analytics. This architectural disconnect prevents banks from seamlessly accessing the transactional information needed for critical functions such as risk modeling and personalized customer targeting. Consequently, the inability to establish a cohesive data environment limits the scalability of analytics initiatives, forcing institutions to rely on manual, error-prone processes that negate the efficiency and speed promised by modern analytical solutions.

This technical barrier directly hampers market growth by elevating the operational risk and expense associated with digital transformation projects. The complexity of layering sophisticated analytics on top of incompatible legacy frameworks often leads to prolonged implementation timelines and ballooning costs, deterring institutions from fully committing to necessary upgrades. According to the Conference of State Bank Supervisors, in 2024, nearly 80 percent of community bankers identified technology implementation and costs as a top internal risk to their organizations. As banks delay these critical technology updates to avoid disruption and financial exposure, the broader adoption of global data analytics stalls, preventing the market from reaching its full potential.

Market Trends

The expansion of open banking and API-driven data ecosystems is fundamentally reshaping the market by transitioning financial institutions from closed, proprietary data silos to collaborative, interoperable networks. This trend allows banks to securely share customer-permissioned data with third-party providers, fostering the development of innovative financial products and streamlined payment services that extend beyond traditional banking interfaces. The acceleration of this ecosystem is evident in the rapid uptake among commercial entities seeking efficiency; according to Mastercard's 'Open banking: The trust imperative' white paper from December 2024, 85 percent of B2B respondents reported currently using open banking solutions to enhance their financial operations. This high adoption rate underscores the market's shift toward platform-based models where data fluidity drives competitive differentiation.

The integration of generative AI for hyper-personalization represents a critical evolution in how banks utilize data, moving beyond static predictive scores to dynamic, conversational customer engagement. Unlike traditional analytics that categorize users into broad segments, generative models analyze individual transaction histories and behavioral nuances to construct bespoke financial advice and automated, human-like interactions in real time. This technological commitment is intensifying as institutions recognize the necessity of AI for operational excellence and customer retention; according to NTT DATA's 'Intelligent Banking in the Age of AI' report from February 2025, 58 percent of banking organizations have fully embraced the transformative potential of generative AI. Such widespread implementation highlights the sector's focus on leveraging advanced algorithms to deliver the tailored, responsive experiences modern consumers demand.

Key Market Players

• International Business Machines Corporation
• SAP SE
• Oracle Corporation
• Microsoft Corporation
• Google LLC
• Amazon Web Services, Inc.
• TIBCO Software, Inc.
• Hewlett Packard Enterprise Co.
• SiSense, Inc.
• Mu Sigma, Inc.
• Dell, Inc.
• Alteryx Inc.
• Teradata Corporation
• Wipro Ltd.
• SAS Institute, Inc.

Report Scope

In this report, the Global Data Analytics in Banking Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Data Analytics in Banking Market, By Deployment Type

• On-Premises
• Cloud

Data Analytics in Banking Market, By Type

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

Data Analytics in Banking Market, By Solution

• Risk Management (Credit Risk Assessment, Fraud Detection and Management, Stress Testing, others)
• Customer Analytics
• Portfolio Management Analytics
• Trading Analytics

Data Analytics in Banking Market, By End User

• Sell Side Firms
• Buy Side Firms

Data Analytics in Banking Market, By Region

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
• South America
  • Brazil
  • Argentina
  • Colombia
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Data Analytics in Banking Market.

Available Customizations:

Global Data Analytics in Banking Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Data Analytics in Banking Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Deployment Type (On-Premises, Cloud)
  • 5.2.2. By Type (Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics)
  • 5.2.3. By Solution (Risk Management (Credit Risk Assessment, Fraud Detection and Management, Stress Testing, others), Customer Analytics, Portfolio Management Analytics, Trading Analytics)
  • 5.2.4. By End User (Sell Side Firms, Buy Side Firms)
  • 5.2.5. By Region
  • 5.2.6. By Company (2025)
• 5.3. Market Map

6. North America Data Analytics in Banking Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Deployment Type
  • 6.2.2. By Type
  • 6.2.3. By Solution
  • 6.2.4. By End User
  • 6.2.5. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Data Analytics in Banking Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Deployment Type
      • 6.3.1.2.2. By Type
      • 6.3.1.2.3. By Solution
      • 6.3.1.2.4. By End User
  • 6.3.2. Canada Data Analytics in Banking Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Deployment Type
      • 6.3.2.2.2. By Type
      • 6.3.2.2.3. By Solution
      • 6.3.2.2.4. By End User
  • 6.3.3. Mexico Data Analytics in Banking Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Deployment Type
      • 6.3.3.2.2. By Type
      • 6.3.3.2.3. By Solution
      • 6.3.3.2.4. By End User

7. Europe Data Analytics in Banking Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Deployment Type
  • 7.2.2. By Type
  • 7.2.3. By Solution
  • 7.2.4. By End User
  • 7.2.5. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany Data Analytics in Banking Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Deployment Type
      • 7.3.1.2.2. By Type
      • 7.3.1.2.3. By Solution
      • 7.3.1.2.4. By End User
  • 7.3.2. France Data Analytics in Banking Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Deployment Type
      • 7.3.2.2.2. By Type
      • 7.3.2.2.3. By Solution
      • 7.3.2.2.4. By End User
  • 7.3.3. United Kingdom Data Analytics in Banking Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Deployment Type
      • 7.3.3.2.2. By Type
      • 7.3.3.2.3. By Solution
      • 7.3.3.2.4. By End User
  • 7.3.4. Italy Data Analytics in Banking Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Deployment Type
      • 7.3.4.2.2. By Type
      • 7.3.4.2.3. By Solution
      • 7.3.4.2.4. By End User
  • 7.3.5. Spain Data Analytics in Banking Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Deployment Type
      • 7.3.5.2.2. By Type
      • 7.3.5.2.3. By Solution
      • 7.3.5.2.4. By End User

8. Asia Pacific Data Analytics in Banking Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Deployment Type
  • 8.2.2. By Type
  • 8.2.3. By Solution
  • 8.2.4. By End User
  • 8.2.5. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China Data Analytics in Banking Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Deployment Type
      • 8.3.1.2.2. By Type
      • 8.3.1.2.3. By Solution
      • 8.3.1.2.4. By End User
  • 8.3.2. India Data Analytics in Banking Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Deployment Type
      • 8.3.2.2.2. By Type
      • 8.3.2.2.3. By Solution
      • 8.3.2.2.4. By End User
  • 8.3.3. Japan Data Analytics in Banking Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Deployment Type
      • 8.3.3.2.2. By Type
      • 8.3.3.2.3. By Solution
      • 8.3.3.2.4. By End User
  • 8.3.4. South Korea Data Analytics in Banking Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Deployment Type
      • 8.3.4.2.2. By Type
      • 8.3.4.2.3. By Solution
      • 8.3.4.2.4. By End User
  • 8.3.5. Australia Data Analytics in Banking Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Deployment Type
      • 8.3.5.2.2. By Type
      • 8.3.5.2.3. By Solution
      • 8.3.5.2.4. By End User

9. Middle East & Africa Data Analytics in Banking Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Deployment Type
  • 9.2.2. By Type
  • 9.2.3. By Solution
  • 9.2.4. By End User
  • 9.2.5. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia Data Analytics in Banking Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Deployment Type
      • 9.3.1.2.2. By Type
      • 9.3.1.2.3. By Solution
      • 9.3.1.2.4. By End User
  • 9.3.2. UAE Data Analytics in Banking Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Deployment Type
      • 9.3.2.2.2. By Type
      • 9.3.2.2.3. By Solution
      • 9.3.2.2.4. By End User
  • 9.3.3. South Africa Data Analytics in Banking Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Deployment Type
      • 9.3.3.2.2. By Type
      • 9.3.3.2.3. By Solution
      • 9.3.3.2.4. By End User

10. South America Data Analytics in Banking Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Deployment Type
  • 10.2.2. By Type
  • 10.2.3. By Solution
  • 10.2.4. By End User
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Data Analytics in Banking Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Deployment Type
      • 10.3.1.2.2. By Type
      • 10.3.1.2.3. By Solution
      • 10.3.1.2.4. By End User
  • 10.3.2. Colombia Data Analytics in Banking Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Deployment Type
      • 10.3.2.2.2. By Type
      • 10.3.2.2.3. By Solution
      • 10.3.2.2.4. By End User
  • 10.3.3. Argentina Data Analytics in Banking Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Deployment Type
      • 10.3.3.2.2. By Type
      • 10.3.3.2.3. By Solution
      • 10.3.3.2.4. By End User

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Data Analytics in Banking Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. International Business Machines Corporation
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. SAP SE
• 15.3. Oracle Corporation
• 15.4. Microsoft Corporation
• 15.5. Google LLC
• 15.6. Amazon Web Services, Inc.
• 15.7. TIBCO Software, Inc.
• 15.8. Hewlett Packard Enterprise Co.
• 15.9. SiSense, Inc.
• 15.10. Mu Sigma, Inc.
• 15.11. Dell, Inc.
• 15.12. Alteryx Inc.
• 15.13. Teradata Corporation
• 15.14. Wipro Ltd.
• 15.15. SAS Institute, Inc.

16. Strategic Recommendations

17. About Us & Disclaimer