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市場調查報告書

全球金融服務的阻礙:機器學習不可或缺

Disruption in Global Financial Services, 2017 - Machine Learning is Imperative

出版商 Frost & Sullivan 商品編碼 539622
出版日期 內容資訊 英文 74 Pages
商品交期: 最快1-2個工作天內
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全球金融服務的阻礙:機器學習不可或缺 Disruption in Global Financial Services, 2017 - Machine Learning is Imperative
出版日期: 2017年07月21日 內容資訊: 英文 74 Pages
簡介

本報告提供金融服務業的機器學習 (ML)影響相關調查,提供金融服務業的發展,ML與其金融服務價值鏈的影響,ML生態系統和各相關利益者,ML解決方案和實行,ML供應商與利用案例等相關資料,分析。

第1章 摘要整理

第2章 市場

第3章 金融服務業的演進

  • 金融服務:過時的決策方法
  • 金融服務:IT必須超越維修
  • 金融服務:IT部門所面臨的課題
  • 金融服務:技術投資的促進要素
  • 金融服務:巨量資料·分析 (BDA) 引進趨勢
  • 金融服務:技術實現的革命

第4章 簡介:機器學習

  • 機器學習:定義·技術
  • 金融服務價值鏈的ML
  • 更智慧的決策:輸出的再調整
  • ML:金融服務的實施

第5章 機器學習

  • 金融服務的ML:TechWheel 對生態系統來說重要
  • 技術主導的生態系統:參與企業的合作
  • 新的生態系統:技術龍頭貢獻
  • 企業簡介:Google
  • 企業簡介:IBM
  • 新的生態系統:通訊企業的貢獻
  • 企業簡介:Orange
  • 企業簡介:Swisscom
  • 新的生態系統:ML新興企業的貢獻
  • 企業簡介:Onfido
  • 企業簡介:Darktrace
  • 企業簡介:AdviceRobo
  • 企業簡介:Rasa.ai
  • 企業簡介:Klarna
  • 新的生態系統:IT企業的貢獻
  • 企業簡介:Infosys
  • 企業簡介:SAP
  • 相關利益者的貢獻分析

第6章 機器學習

  • 金融服務ML解決方案
  • ML解決方案:金融服務的應用
  • 預測分析:趨勢
  • 違法檢測·ID管理:趨勢
  • 聊天機器人:趨勢
  • 模式認識:趨勢
  • 資訊發現·開採:趨勢
  • 金融服務的ML技術趨勢

第7章 金融服務的機器學習的引進

  • 金融服務的ML的引進:市場成長的促進要素
  • 促進要素的說明
  • 金融服務的ML引進:市場抑制因素
  • 抑制因素的說明

第8章 成長機會及企業趨勢

  • 5大成長機會
  • 成長機會1:防止詐欺
  • 成長機會2:信用度評估
  • 成長機會3:機器人顧問
  • 成長機會4:RegTech (legTec)
  • 成長機會5:網路安全
  • 成功·成長的策略性必要條件

第9章 結論

第10章 FROST & SULLIVAN

目錄
Product Code: MD13-33

Realigning Customer Engagement with Predictive Analytics and Customization

Technology is disrupting the financial services industry. Also termed fintech, tech-enabled products and services in the industry are further enhanced by advanced technologies such as cloud, IoT, analytics, artificial intelligence (AI), and machine language (ML). This research service explores the impact of ML on the financial services industry. The objectives of the study are to understand the following:

  • The evolution of the financial services industry
  • ML and its impact on the financial services value chain
  • The ML ecosystem and different stakeholders
  • ML solutions and their implementation
  • Providers and use cases of ML

Shared economy and connected devices have made Big Data ubiquitous, and analytics has improved the outcomes of data analysis. To ensure that all the available data is utilized to come up with insights, an increase in the adoption of ML is expected, which would several processes and increase the ease of data gathering and analysis. Companies are experimenting with and adopting new ML-enabled business models, solutions, and services, and entering new markets. Fraud prevention, robo-advisory services and credit scoring are some of the largest growth opportunities for the application of ML in financial services. As proofs of concept and use cases come to the fore, myriad applications of ML are expected to alter the financial services industry as it is known today.

Different stakeholders in the industry use diverse methods to implement it, including the following:

  • Start-ups are introducing innovation into the system by offering financial services that are cost-effective, faster, automated, and take into account consumer behaviour.
  • Large tech companies such as Amazon and Apple realize the potential and are already offering payment solutions to consumers.
  • IT companies responsible for the vast IT systems in financial institutions are upgrading their offerings with innovative and advanced technologies.
  • With connectivity playing an important role in creating an ecosystem that makes financial services available to consumers 24x7, telecom companies are also increasing their presence by updating their offers and including ML.

Following are some of the key questions the study answers:

  • What are the challenges within the financial services industry that ML can help mitigate?
  • What are the current trends in ML adoption?
  • What drivers will encourage ML in financial services?
  • What are the restraining factors that may affect the growth of ML adoption?
  • What are the growth opportunities for ML in financial services?

ML in financial services is forecast to become mainstream in a few years, as many factors are driving adoption. Notwithstanding all the challenges, the importance of ML is clear, and the inclusion imperative for financial services to successfully meet consumer demands and create an efficient and effective system.

Table of Contents

1. EXECUTIVE SUMMARY

  • Key Findings

2. MARKET

  • Definitions
  • Definitions (continued)

3. EVOLUTION OF THE FINANCIAL SERVICES INDUSTRY

  • Financial Services-Obsolete Approach to Decision Making
  • Financial Services-IT Needs to Move Beyond Maintenance
  • Financial Services-Challenges Faced by IT Departments
  • Financial Services-Driving Investment in Technology
  • Financial Services-Big Data and Analytics (BDA) Adoption Trend
  • Financial Services-Technology-enabled Evolution

4. INTRODUCTION-MACHINE LEARNING

  • Machine Learning-Definition and Techniques
  • ML in Financial Services Value Chain
  • Smarter Decisions-Realigning Output
  • ML-Implementation in Financial Services

5. MACHINE LEARNING

  • ML in Financial Services-TechWheel Critical to Ecosystem
  • Technology Driven Ecosystem-Participants Collaborate
  • Technology Driven Ecosystem-Participant Collaborations (continued)
  • Technology Driven Ecosystem-Participant Collaborations (continued)
  • New Ecosystem-Contribution of Tech Majors
  • Company Profile-Google
  • Company Profile-IBM
  • New Ecosystem-Contribution of Telecom Companies
  • Company Profile-Orange
  • Company Profile-Swisscom
  • New Ecosystem-Contribution of ML Start-ups
  • Company Profile-Onfido
  • Company Profile-Darktrace
  • Company Profile-AdviceRobo
  • Company Profile-Rasa.ai
  • Company Profile-Klarna
  • New Ecosystem-Contribution of IT Companies
  • Company Profile-Infosys
  • Company Profile-SAP
  • Stakeholder Contribution Analysis

6. MACHINE LEARNING

  • ML Solutions for Financial Services
  • ML Solutions-Applications in Financial Services
  • Predictive Analytics-Trends
  • Fraud Detection and Identity Management-Trends
  • Chatbots-Trends
  • Pattern Recognition-Trends
  • Information Discovery and Extraction-Trends
  • ML Technology Trends in Financial Services

7. ADOPTION OF MACHINE LEARNING IN FINANCIAL SERVICES-DRIVERS AND RESTRAINTS

  • ML Adoption in Financial Services-Market Drivers
  • Drivers Explained
  • Drivers Explained (continued)
  • ML Adoption in Financial Services-Market Restraints
  • Restraints Explained

8. GROWTH OPPORTUNITIES AND COMPANIES TO ACTION

  • 5 Major Growth Opportunities
  • Growth Opportunity 1-Fraud Prevention
  • Growth Opportunity 2-Credit Scoring
  • Growth Opportunity 3-Robo-advisory
  • Growth Opportunity 4-RegTech
  • Growth Opportunity 5-Cybersecurity
  • Strategic Imperatives for Success and Growth

9. THE LAST WORD

  • The Last Word-3 Big Predictions
  • Legal Disclaimer

10. THE FROST & SULLIVAN STORY

  • The Frost & Sullivan Story
  • Value Proposition-Future of Your Company & Career
  • Global Perspective
  • Industry Convergence
  • 360° Research Perspective
  • Implementation Excellence
  • Our Blue Ocean Strategy
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