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市場調查報告書

金融服務行業的巨量資料:市場分析與預測 (2015∼2020年)

Big Data in Financial Services Industry: Market Analysis and Forecasts 2015 - 2020

出版商 Mind Commerce 商品編碼 316728
出版日期 內容資訊 英文 98 Pages
商品交期: 最快1-2個工作天內
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金融服務行業的巨量資料:市場分析與預測 (2015∼2020年) Big Data in Financial Services Industry: Market Analysis and Forecasts 2015 - 2020
出版日期: 2015年12月30日 內容資訊: 英文 98 Pages
簡介

金融服務行業 (信用合作社、銀行、信用卡企業、保險企業、會計事務所、消費者金融、證券會社、投資基金等) ,為巨量資料核心領域之一,各個部門都有巨大的市場機會。巨量資料和附屬的分析解決方案帶給金融服務行業者許多利益 (費用最佳化、風險管理、信用管理、投資建議、個體化服務、運用效率改善、詐騙防止與安全、客服中心運用效率改善、個人客人服務改善、遵守遵守等)。透過有效利用巨量資料和相關技術,可在金融服務企業補充、分析資料,構築預測模式和回溯測試,模擬方案。還有各企業經過了反復學習之後,可決定最重要的變數和主要預測模式。金融服務行業者現在正在學習強化競爭力和最小化成本,轉換風險機會的方法,即時最小化風險的方法等。

本報告提供全球金融服務行業取向巨量資料市場未來展望與市場機會相關分析、巨量資料引進帶來的影響,及主要巨量資料、服務供應商的概要、主要的金融服務管理模式與其引進方法、金融服務業帶來的短期與長期性的優點、巨量資料引進時應解決的課題等調查&考察。

第1章 摘要整理

第2章 金融服務行業的巨量資料

  • 金融服務行業
  • 巨量資料造成金融服務行業的轉換
  • 金融服務行業中巨量資料的宏觀推動因素
    • 無現金社會
    • 在貿易、投資領域的遵守、行動學習的巨量資料
    • 金融服務的政府和巨量資料
  • 金融服務中巨量資料所扮演的角色
  • 巨量資料正逐漸成為金融服務部門不可或缺的因素
  • 巨量資料的金融服務市場的3個方法
    • 資訊為基礎的區分
    • 資訊為基礎的仲介業務
    • 資訊為基礎的服務提供
  • 為了讓巨量資料在金融服務業發揮作用的階段
    • 資料取得、收集、偵測
    • 資料管理、整合
    • 資料分析
  • 在金融服務的市場競爭的差異化要素的巨量資料
  • 金融相關的巨量資料管理:參照資料
  • 在金融服務行業的巨量資料的未來性

第3章 金融服務行業者的巨量資料的措施

  • 在金融服務業的目前巨量資料引進情形
    • 金融服務行業者的巨量資料的措施
  • 在金融服務業的巨量資料相關的主要配合措施
    • 對於客戶的聯絡方式的即時回答
    • 使用了預測分析功能,客戶的行動、趨勢資料的評估
    • 客戶的心理狀態的測量與恰當行動的實施
    • 資料重組的大量客製化
    • 巨大收益的巨量資料
    • 為了預見詐騙和其他金融犯罪的巨量資料

第4章 金融服務行業的巨量資料:全球市場的未來趨勢

  • 全球巨量資料市場
    • 非結構化資料市場
    • 「第三方平台」預測
    • 資料流程市場影響度
    • 皆位元組市場趨勢
    • 全球巨量資料市場未來發展預測 (今後6年份)
    • 資料分析:市場競爭的最前線
  • 金融服務業從巨量資料學習到的事
  • 金融業巨量資料的全球市場 (今後6年份)
  • 金融服務行業會的重點投資領域 (今後6年份)

第5章 企業與解決方案

  • 巨量資料財務管理解決方案
  • 企業、解決方案
    • 1010DATA
    • 10GEN
    • ACTIAN
    • ALTERYX
    • AMAZON
    • ATTIVIO
    • BOOZ ALLEN HAMILTON
    • CAPGEMINI
    • CISCO SYSTEMS
    • CLOUDERA
    • CSC
    • DELL
    • EMC
    • FUSION-IO
    • GOODDATA
    • GOOGLE
    • GUAVUS
    • HP
    • 日立製作所
    • IBM
    • INFORMATICA
    • INTEL
    • MARKLOGIC
    • MICROSOFT
    • MU SIGMA
    • NETAPP
    • OPERA SOLUTIONS
    • ORACLE
    • PARACCEL
    • QLIKTECH
    • SAP
    • SGI
    • SPLUNK
    • TERADATA
    • TIBCO SOFTWARE
    • VMWARE

第7章 結論與建議

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目錄

Overview:

Financial Services (credit unions, banks, credit-card companies, insurance companies, accountancy companies, consumer-finance companies, stock brokerages, investment funds) is one of the key areas for Big Data as there is great benefits for the entire ecosystem. Big Data and various analytics solutions provide many benefits to financial services organizations including: Optimizing Pricing, Risk Management, Credit Worthiness, Investment Advice, Personalized Offerings, Better Operational Efficiency, Fraud Detection and Security, Improved Call Center Operations, Better Customer Insight and Service, Governance and Regulatory Compliance.

Big Data technologies and related business intelligence solutions provide financial services firms with the capability to capture and analyze data, build predictive models, back-test and simulate scenarios. Through iteration, firms will determine the most important variables and also key predictive models. Financial service providers are learning to leverage the value of data and gain competitive advantage, minimize costs, convert challenges to opportunities, and minimize risk in real-time.

This research evaluates the market for Big Data in the financial services sector, analyzes key players, identifies challenges and opportunities, and provides forecasting for 2015 to 2020. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

  • Big Data companies
  • Telecom service providers
  • Regulatory and policy makers
  • Data services and analytics companies
  • Cloud and telecom infrastructure providers
  • Financial services companies and personnel
    • Hedge Funds
    • Private Equity
    • Asset Managers
    • Financial Analysts
    • Investor Relations
    • Portfolio Managers
    • Investment Bankers

Report Benefits:

  • Big Data in financial services forecasting through 2020
  • Identify leading companies and solutions for financial sector
  • Identify the Top Reasons why financial institutions need Big Data
  • Understand the government needs for Big Data in financial sector
  • Understand the role and importance of Big Data in financial services
  • Recognize the future prospects for Big Data in financial services industry
  • Identify initial and ongoing implementation areas for Big Data and analytics

Table of Contents

1.0. EXECUTIVE SUMMARY

2.0. BIG DATA IN FINANCIAL SERVICES

  • 2.1. FINANCIAL SERVICES INDUSTRY
  • 2.2. FINANCIAL SERVICES TRANSFORMS WITH BIG DATA
  • 2.3. MACRO DRIVERS FOR BIG DATA IN FINANCIAL SERVICE
    • 2.3.1. CASHLESS SOCIETY
    • 2.3.1. BIG DATA IN TRADING AND INVESTING COMPLIANCE AND BEHAVIOR LEARNING
    • 2.3.2. THE GOVERNMENT AND BIG DATA IN FINANCIAL SERVICES
  • 2.4. ROLE OF BIG DATA IN FINANCIAL SERVICES
  • 2.5. BIG DATA TO BECOME ESSENTIAL COMPONENT FOR FINANCIAL SERVICE SECTOR
  • 2.6. A THREE-WAY BIG DATA APPROACH TOWARDS FINANCIAL SERVICES
    • 2.6.1. INFORMATION BASED SORTING
    • 2.6.2. INFORMATION BASED BROKERING
    • 2.6.3. INFORMATION BASED DELIVERY
  • 2.7. STEPS FOR BIG DATA FUNCTIONING IN FINANCIAL SERVICES
    • 2.7.1. DATA ACQUISITION, COLLECTION, AND DETECTION
    • 2.7.2. DATA MANAGEMENT AND INTEGRATION
    • 2.7.3. DATA ANALYSIS
  • 2.8. BIG DATA AS COMPETITIVE DIFFERENTIATOR FOR FINANCIAL SERVICES
  • 2.9. FINANCIAL BIG DATA MANAGEMENT: REFERENCE DATA
  • 2.10. FUTURE OF BIG DATA IN FINANCIAL SECTOR

3.0. BIG DATA INITIATIVES OF FINANCIAL SERVICES PROVIDERS

  • 3.1. CURRENT STAGE OF THE BIG DATA IMPLEMENTATION IN FINANCIAL SERVICES
    • 3.1.1. FINANCIAL SERVICE PROVIDER BIG DATA INITIATIVES
  • 3.2. TOP BIG DATA INITIATIVES IN FINANCIAL SERVICES SECTOR
    • 3.2.1. PROVIDE REAL-TIME RESPONSE TO CONSUMER QUERIES
    • 3.2.2. ASSESS CUSTOMER BEHAVIORAL AND TENDENCY DATA USING PREDICTIVE ANALYTICS
    • 3.2.3. MEASURE CUSTOMER SENTIMENTS AND TAKE APPROPRIATE ACTION
    • 3.2.4. MASS CUSTOMIZATION DATA REMODELING
    • 3.2.5. BIG DATA FOR BIG REVENUE
    • 3.2.6. BIG DATA FOR PREDICTING FRAUD AND OTHER FINANCIAL CRIMES

4.0. BIG DATA IN FINANCIAL SERVICES: GLOBAL MARKET 2015 - 2020

  • 4.1. THE GLOBAL BIG DATA MARKET
    • 4.1.1. THE UNSTRUCTURED DATA MARKET
    • 4.1.2. THE THIRD PLATFORM PERSPECTIVE
    • 4.1.3. DATA PROCESS MAGNITUDE
    • 4.1.4. TOWARDS THE ZETTABYTES MARKET
    • 4.1.5. GLOBAL MARKETS FOR BIG DATA 2015 - 2020
    • 4.1.6. DATA ANALYTICS IS THE BATTLEGROUND FOR COMPETITION
  • 4.2. LEARNING FROM BIG DATA IN FINANCIAL SERVICES SECTOR
  • 4.3. GLOBAL MARKET FOR BIG DATA IN FINANCIAL SECTOR 2015 - 2020
  • 4.4. FOCUS AREAS FOR FINANCIAL SERVICES SECTOR INVESTMENT 2015 - 2020

5.0. COMPANIES AND SOLUTIONS

  • 6.1. BIG DATA FINANCIAL MANAGEMENT SOLUTIONS
  • 6.2. COMPANIES AND SOLUTIONS
    • 6.2.1. 1010DATA
    • 6.2.2. 10GEN
    • 6.2.3. ACTIAN
    • 6.2.4. ALTERYX
    • 6.2.5. AMAZON
    • 6.2.6. ATTIVIO
    • 6.2.7. BOOZ ALLEN HAMILTON
    • 6.2.8. CAPGEMINI
    • 6.2.9. CISCO SYSTEMS
    • 6.2.10. CLOUDERA
    • 6.2.11. CSC
    • 6.2.12. DELL
    • 6.2.13. EMC
    • 6.2.14. FUSION-IO
    • 6.2.15. GOODDATA
    • 6.2.16. GOOGLE
    • 6.2.17. GUAVUS
    • 6.2.18. HP
    • 6.2.19. HITACHI
    • 6.2.20. IBM
    • 6.2.21. INFORMATICA
    • 6.2.22. INTEL
    • 6.2.23. MARKLOGIC
    • 6.2.24. MICROSOFT
    • 6.2.25. MU SIGMA
    • 6.2.26. NETAPP
    • 6.2.27. OPERA SOLUTIONS
    • 6.2.28. ORACLE
    • 6.2.29. PARACCEL
    • 6.2.30. QLIKTECH
    • 6.2.31. SAP
    • 6.2.32. SGI
    • 6.2.33. SPLUNK
    • 6.2.34. TERADATA
    • 6.2.35. TIBCO SOFTWARE
    • 6.2.36. VMWARE

7.0. CONCLUSIONS AND RECOMMENDATIONS

Figures

  • Figure 1: Big Data Approaches for Financial Services
  • Figure 2: Big Data Functional Levels
  • Figure 3: Big Data as Competitive Differentiator for Financial Services
  • Figure 4: Financial Big Data Management Paradigm
  • Figure 5: Big Data for Predicting Financial Crimes
  • Figure 6: Big Data Paradigm
  • Figure 7: Migration Process of Platform Technology
  • Figure 8: Data Universe Zettabytes Generation 2013 - 2020
  • Figure 9: Global Big Data Market Forecast 2015 - 2020
  • Figure 10: Global BD Market by H/W, S/W, and Services 2015 - 2020
  • Figure 11: Big Data in Financial Services by Components 2015 - 2020
  • Figure 12: Big Data Revenue Share by Vendor Solutions
  • Figure 13: Hadoop and NoSQL Vendor Revenue Share

Tables

  • Table 1: Global Big Data Market 2015 - 2020
  • Table 2: Global Big Data Markets by H/W, S/W, and Services 2015 - 2020
  • Table 3: Global Markets for Big Data in Financial Sector
  • Table 4: 1010data Big Data Financial Management Solutions
  • Table 5: 10gen Big Data Financial Management Solutions
  • Table 6: Actian Big Data Financial Management Solutions
  • Table 7: Alteryx Big Data Financial Management Solutions
  • Table 8: Amazon Big Data Financial Management Solutions
  • Table 9: Attiivio Big Data Financial Management Solutions
  • Table: 10 Booz Allen Hamilton Big Data Financial Management Solutions
  • Table 11: Capgemini Big Data Financial Management Solutions
  • Table 12: Cisco Big Data Financial Management Solutions
  • Table 13: Cloudera Big Data Financial Management Solutions
  • Table 14: CSC Big Data Financial Management Solutions
  • Table 15: Dell Big Data Financial Management Solutions
  • Table 16: EMC Big Data Financial Management Solutions
  • Table 17: Fusion-IO Big Data Financial Management Solutions
  • Table 18: GoodData Big Data Financial Management Solutions
  • Table 19: Google Big Data Financial Management Solutions
  • Table 20: Guavus Big Data Financial Management Solutions
  • Table 21: HP Big Data Financial Management Solutions
  • Table 22: Hitachi Big Data Financial Management Solutions
  • Table 23: IBM Big Data Financial Management Solutions
  • Table 24: Informatica Big Data Financial Management Solutions
  • Table 25: Intel Big Data Financial Management Solutions
  • Table 26: MarkLogic Big Data Financial Management Solutions
  • Table 27: Microsoft Big Data Financial Management Solutions
  • Table 28: Mu Sigma Big Data Platforms
  • Table 29: MuSigma Big Data Financial Management Solutions
  • Table 30: NetApp Big Data Financial Management Solutions
  • Table 31: Opera Solutions Big Data Financial Management Solutions
  • Table 32: Oracle Big Data Financial Management Solutions
  • Table 33: ParAccel Big Data Financial Management Solutions
  • Table 34: Qlick Tech Big Data Financial Management Solutions
  • Table 35: SAP Big Data Financial Management Solutions
  • Table 36: SGI Big Data Financial Management Solutions
  • Table 37: Splunk Big Data Financial Management Solutions
  • Table 38: Teradata Big Data Financial Management Solutions
  • Table 39: Tibco Software Big Data Financial Management Solutions
  • Table 40: VMware Big Data Financial Management Solutions
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