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市場調查報告書
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1914694

銀行業資料分析市場－全球產業規模、佔有率、趨勢、機會及預測（按部署類型、類型、解決方案、最終用戶、地區和競爭格局分類，2021-2031年）

Data Analytics in Banking Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Deployment Type, By Type, By Solution, By End User, By Region & Competition, 2021-2031F
出版日期: | 出版商: TechSci Research | 英文 181 Pages | 商品交期: 2-3個工作天內

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簡介目錄

全球銀行業數據分析市場預計將從2025年的132.9億美元顯著成長至2031年的387.4億美元，複合年成長率（CAGR）達19.52%。數據分析是對財務記錄進行系統性計算檢驗，它使銀行能夠識別模式、關聯性和趨勢，從而指導策略決策。該市場的主要驅動力是迫切需要健全的風險管理框架以及日益成長的個人化客戶體驗需求，這兩方面都要求金融機構快速處理大量交易資訊。此外，嚴格的監管合規要求迫使金融機構實施嚴謹的分析措施，以確保透明度並預防金融犯罪，這也是推動數據分析在銀行業廣泛應用的根本原因。

市場概覽
預測期 2027-2031
市場規模：2025年 132.9億美元
市場規模：2031年 387.4億美元
複合年成長率：2026-2031年 19.52%
成長最快的細分市場 雲
最大的市場 北美洲

儘管有這些成長要素，但阻礙市場擴張的一大挑戰在於難以將現代分析工具與分散的傳統IT基礎設施整合，這往往導致資料孤島和管治問題。隨著產業在數據通訊協定規範化方面舉步維艱，這種營運脫節現象尤其明顯。根據美國銀行家協會（ABA）預測，到2024年，71%的銀行負責人表示其所在機構缺乏成文的客戶資料策略。這種策略規劃的缺失阻礙了銀行充分利用其數據資產，最終延緩了全球分析市場的整體成熟度。

市場促進因素

人工智慧 (AI) 和機器學習 (ML) 的整合是推動市場發展的關鍵因素，使金融機構能夠從事後分析轉向預測智慧。銀行正在利用這些技術處理非結構化資料集，以實現自動信用評分和演算法產品推薦。這項技術轉型得益於高普及率：NVIDIA 於 2024 年 2 月發布的《金融服務業人工智慧現狀：2024 年趨勢》報告顯示，91% 的金融服務公司正在評估或實施人工智慧，以增強創新能力和業務連續性。如此廣泛的整合需要能夠處理複雜模型的強大分析平台，而金融機構透過數據驅動的前瞻性來維持競爭優勢的努力，正是推動市場成長的動力。

同時，對即時詐欺偵測日益成長的需求迫使銀行部署能夠在毫秒內識別異常情況的尖端分析解決方案。隨著交易量的成長，傳統的基於規則的系統已無法應對不斷演變的網路威脅，因此行為分析和模式識別至關重要。這些措施的影響是可以量化的：根據Visa於2024年3月發布的《2024年春季威脅報告》，該銀行的分析能力在上年度阻止了價值400億美元的非法貿易。為了支持這些安全措施和廣泛的數位基礎設施，大量資金正投入技術升級。摩根大通在2024年撥款約170億美元用於技術，凸顯了以數據為中心的投資的重要性。

市場挑戰

市場擴張面臨的一大挑戰在於，難以將現代分析工具與分散的傳統IT基礎設施整合，導致資料孤島大規模，管治漏洞百出。金融機構往往依賴老舊的核心系統，這些系統無法有效率地與新型資料密集型應用對接，幾乎不可能聚合​​高級分析所需的整合即時資料集。這種架構上的脫節使得銀行無法無縫取得風險建模和個人化客戶定位等關鍵功能所需的交易資訊。因此，無法創建一致的數據環境限制了分析舉措的擴充性，迫使金融機構依賴人工操作且容易出錯的流程，從而削弱了現代分析解決方案所承諾的效率和速度。

這種技術壁壘直接阻礙了市場成長，因為它增加了數位轉型計劃相關的營運風險和成本。在不相容的傳統框架上建立高階分析功能的複雜性導致實施時間過長、成本過高，使得金融機構無法全面進行必要的升級。根據州銀行監管機構協會 (CSBS) 預測，到 2024 年，約 80% 的社區銀行將把技術採用和成本視為其機構面臨的主要內部風險。由於銀行為了避免業務中斷和財務風險而推遲這些關鍵的技術升級，全球數據分析的採用進程停滯不前，阻礙了市場充分發揮其潛力。

市場趨勢

開放銀行和API驅動的資料生態系統的擴展正在從根本上重塑市場，推動金融機構從封閉的專有資料孤島走向協作互通的網路。這一趨勢使得銀行能夠在獲得客戶許可的情況下安全地與第三方供應商共用數據，從而促進創新金融產品和簡化支付服務的開發，超越了傳統的銀行介面。商業機構對此生態系統的快速採用也印證了其加速發展。根據萬事達卡2024年12月發布的白皮書《開放銀行：信任的必要性》，85%的B2B受訪者表示目前正在使用開放銀行解決方案來提升其財務營運效率。如此高的採用率支持市場向平台模式轉型，在這種模式下，數據的流動性將驅動競爭優勢。

將生成式人工智慧融入超個人化服務，標誌著銀行利用數據方式的重大變革，使其超越靜態預測評分，並邁向動態的互動式客戶參與。與將使用者粗略分類的傳統分析方法不同，生成式模型能夠分析個人交易歷史和行為的細微差別，從而創造即時、量身定做的金融諮詢和自動化的類人互動。隨著金融機構逐漸意識到人工智慧對於提升營運效率和客戶維繫至關重要，這項技術正蓬勃發展。 NTT DATA 於 2025 年 2 月發布的報告《人工智慧時代的智慧銀行》顯示，58% 的銀行機構已全面擁抱生成式人工智慧的變革潛力。如此廣泛的應用凸顯了銀行業日益重視利用先進演算法，為現代消費者提供他們所期望的客製化、響應式體驗。

目錄

第1章概述

第2章調查方法

第3章執行摘要

第4章：客戶評價

第5章：全球銀行業數據分析市場展望

  • 市場規模及預測
    • 按金額
  • 市佔率及預測
    • 依部署類型（本機部署、雲端部署）
    • 依類型（說明分析、診斷性分析、預測性分析、指示性分析）
    • 按解決方案（風險管理（信用風險評估、詐欺偵測與管理、壓力測試等）、客戶分析、投資組合管理分析、交易分析）
    • 依最終用戶（賣家、買家）分類
    • 按地區
    • 按公司（2025 年）
  • 市場地圖

6. 北美銀行業數據分析市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 北美洲：國家分析
    • 美國
    • 加拿大
    • 墨西哥

7. 歐洲銀行業數據分析市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙

8. 亞太地區銀行業資料分析市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

9. 中東和非洲銀行業資料分析市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

第10章：南美銀行業資料分析市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷

第11章 市場動態

  • 促進要素
  • 任務

第12章 市場趨勢與發展

  • 併購
  • 產品發布
  • 最新進展

第13章 全球銀行業資料分析市場：SWOT分析

第14章：波特五力分析

  • 產業競爭
  • 新進入者的可能性
  • 供應商電力
  • 顧客權力
  • 替代品的威脅

第15章 競爭格局

  • 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16章 策略建議

第17章：關於研究公司及免責聲明

簡介目錄
Product Code: 7708

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.

Market Overview
Forecast Period2027-2031
Market Size 2025USD 13.29 Billion
Market Size 2031USD 38.74 Billion
CAGR 2026-203119.52%
Fastest Growing SegmentCloud
Largest MarketNorth America

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