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市場調查報告書

金融科技的人工智能和機器學習:動態、阻礙、未來市場機會 (2016-2021年)

AI (Artificial Intelligence) & Machine Learning - Fintech: Dynamics, Disruption & Future Opportunities 2016-2021

出版商 Juniper Research 商品編碼 365304
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
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金融科技的人工智能和機器學習:動態、阻礙、未來市場機會 (2016-2021年) AI (Artificial Intelligence) & Machine Learning - Fintech: Dynamics, Disruption & Future Opportunities 2016-2021
出版日期: 2016年08月02日 內容資訊: 英文
簡介

本報告提供FinTech (金融科技) 部門的AI (人工智能) 及機器學習技術的使用趨勢的相關調查,AI (人工智能)的定義,目前使用概況 ,投資環境,未來展望,成長推進因素與課題,Fintech的各種利用案例,AI供應商環境,主要供應商簡介,機器學習的引進趨勢及預測等彙整資料。

第1章 關於AI (人工智能)

  • 簡介
  • AI定義
  • AI的目前情形
    • 里程碑
    • 目前的利用
    • 投資環境
      • 案例研究:Viv
      • 案例研究:Poncho
  • 未來的AI
    • 短期的未來
      • 案例研究:Nvidia Tesla Pl00
      • 案例研究:Google TPU
    • 長期的未來
      • 案例研究:IBM True North
  • AI的各種趨勢、推進因素、規定
    • 推進因素
      • 技術方面
      • 客戶
      • 供應商
    • 趨勢
      • 技術方面
      • 客戶
      • 供應商
    • 規定
      • 技術方面
      • 客戶
      • 供應商

第2章 Fintech的AI:市場動態

  • Fintech的AI
    • 財務建議
      • AI干擾器:案例研究 - Sentient Technologies
    • 智慧錢包
      • AI干擾器:案例研究 - Wallet.AI
    • 未來受到影響的領域
    • 投資環境
    • 在金融業界的AI展望
    • 金融AI:要點

第3章 AI供應商環境

  • 簡介
  • AI干擾器&挑戰者的象限
  • Juniper Leaderboard
    • 規定、解釋
  • Juniper Leaderboard:評分結果
    • 相關利益者分組
  • 有影響力的組織
  • 業者簡介
    • Alphabet
    • Amazon
    • Apple
    • Baidu
    • Facebook
    • IBM
    • Intel
    • Microsoft
    • Nvidia
    • Samsung Electronics
    • Salesforce.com
    • Twitter
      • 企業簡介
      • 營業地區
      • 主要客戶、合作夥伴
      • 產品與服務
      • Juniper的見解等

第4章 AI的Fintech:機器學習的預測

  • 簡介
  • 預測手法、前提條件
  • 無擔保融資發行企業
  • 機器學習技術的無擔保融資
  • 融資總額
  • 平台收益
目錄

Overview

The availability and affordability of near-unlimited computing resources alongside Big Data and connectivity has led to a boom in interest in machine learning, a subset of AI (artificial intelligence).

Juniper's incisive research provides unique insights into how, and why AI is being integrated into the Fintech space, and what it means for the industry.

This research suite comprises:

  • Market Trends & Competitive Landscape (PDF)
  • 5 Year Market Sizing & Forecast (PDF & Excel)

Key Features

  • Assessment of impacted industry areas
  • Investment landscape analysis
  • Outlook for AI in Fintech via Juniper's Phased Evolution analysis
  • Case studies highlighting player activity and strategy
  • AI trends, drivers and constraints
  • AI tomorrow: near and long-term analysis
  • AI Disruptors & Challengers Quadrant
  • AI Leaderboard Analysis
  • Benchmark forecasts for adoption and platform spend

Key Questions

  • 1. At what pace are platform revenues for AI driven services anticipated to rise?
  • 2. What are the applications for AI in Fintech, and what will be their impact?
  • 3. Who are the key disruptors in this space, and what strategies are vendors employing?
  • 4. What are the key market forces acting on the AI industry?
  • 5. How is the industry expected to develop towards 2021 and beyond?

Companies Referenced

Interviewed: Kabbage, Nvidia, Rocket Fuel, Sentient Technologies, Sentrian, Zephyr Health.

Profiled: Alphabet, Amazon, Apple, Baidu, Facebook, Intel, Microsoft, Nvidia, Salesforce.com, Samsung, Twitter.

Case Studied: Facebook, Google, IBM, Nvidia, Sentient Technologies, Viv Labs, Wallet.AI.

Mentioned:

AdRoll, Aging Analytics, AICure, Aidya, Akamai Technologies, AlchemyAPI, Alibaba, Altera, Amobee, Aorato, API.ai, AppNexus, Associated Press, AT&T, Audi, Automated Insights, Ayasdi, Banjo, Behold.ai, Betterment, BioBeats, BlackRock, BMW, Bosch, Boston Dynamics, Capitol Health, Charles Schwab, Cisco, Civil Maps, Clear Channel UK, ClevAPI, Cognea, Comma, Cortex, Coursera, Criteo, Cypress, Daimler, Dark Blue Labs, Datacratic, Deep 6, Deep Genomics, Demandware, DFKI, DNNresearch, DriveAI, D-Wave, Elixir Studios, Emotient, Emu, Enlitic, Equivio, Esri, Explorys, FitBit, FiveAI, FlexIC, Ford, Forensiq, Gauss Surgical, Gemalto, Ginger.io, GM, Granata Decision Systems, Healint, Hewlett Packard, Hound, Hughes Research Lab, Hyundai, Idibon, IEEE, Implisit, Indisys, Inria, IPSoft, IRIS Analytics, Itseez, Jaguar Land Rover, Jetpac, Jvion, Kasisto, Kiva Systems, Kosei, Kreditech, LendingClub, LinkedIn, Lockheed Martin, Lumiata, Lunit, Madbits, Magic Pony Technology, Maluuba, MasterCard, MediaMath, MedWhat, Mercedes, Merge Healthcare, MetaMind, MindMeld, MinHash, Mobileye, Moodstocks, Movidius, Nanigans, Narrative Science, NASA, Nauto, NHS (National Health Service), NHSTA (National Highway Safety Transport Administration), Numerai, NVCA (National Venture Capital Association), Nvidia, Onfido, Orbeus, Palantir Technologies, Pebble, Pebbles, Perceptio, Persado, Phytel, Pitney Bowes, Pizza Hut, Posterscope, PredictionIO, Profitero, Publicis Groupe, Quanergy, RBS, Reactor Labs, Real Life Analytics, RelateIQ, Revolution Analytics, Safaba Translation Solutions, Saffron AI, SAP, Scalable Capital, Softbank, Su, SupplyChainBrain, Surreal Vision, SwiftKey, Synapsify, Synthace, Telegram, TellApart, Tempo AI, Tesla, The Weather Company, Timeful, ToolsGroup, Touchkin, Toyota, Triggit, Truven Health Analytics, TSMC, Turn, Vanguard, Vicarious, Vision Factory, VisualGraph, VitreosHealth, Vivendi Universal, Vivint, VocalIQ, VoiceBox, Volvo, W3C (World Wide Web Consortium), WaferTech, Wand Labs, Wealthfront, Whetlab, Wink, Wit.ai, Wonga, X.ai, Xerox, YouTube, ZestFinance, Zoox.

Data & Interactive Forecast

Juniper's Fintech AI forecast suite includes:

  • Machine learning spend:
    • Consumers with access to online banking services
    • Unsecured Consumer Loans Issued Using Machine Learning Technology
    • Total Loan Value
    • Loan Issuer Platform Revenues
  • Regional splits for 8 key regions, as well as country level data splits for:
    • Canada
    • Germany
    • Japan
    • South Korea
    • UK
    • US
  • Interactive Scenario Tool allowing users to manipulate Juniper's data for 3 different metrics.
  • Access to the full set of forecast data of 13 tables and over 1320 datapoints.

Juniper Research's highly granular interactive excels enable clients to manipulate Juniper's forecast data and charts to test their own assumptions using the Interactive Scenario Tool; and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

1. Demystifying Artificial Intelligence

  • 1.1. Introduction
    • Figure 1.1: ELIZA
  • 1.2. Defining AI
    • Figure 1.2: Principal Goals of AI Systems
    • 1.2.1. Building Blocks of AI
      • Figure 1.3: Sussman Anomaly
    • 1.2.2. Definition
  • 1.3. The State of AI Today
    • 1.3.1. AI Milestones
    • 1.3.2. AI in Use Today
    • 1.3.3. AI Investment Landscape
      • ii. Case Study: Viv
        • Figure 1.4: Poncho via Messenger Platform
      • iv. Case Study: Poncho
        • Figure 1.5: AI VC Investment 2010-2015
  • 1.4. AI Tomorrow
    • 1.4.1. Near-Term
      • i. Case Study: Nvidia Tesla Pl00
      • ii. Case Study: Google TPU
    • 1.4.2. Long-Term
      • i. Case Study: IBM True North
  • 1.5. AI Trends, Drivers & Constraints
    • Figure 1.6: AI Trends, Drivers & Constraints
    • 1.5.1. Drivers
      • i. Technological
      • ii. Customer
      • iii. Vendor
    • 1.5.2. Trends
      • i. Technological
      • ii. Customer
      • iii. Vendor
    • 1.5.3. Constraints
      • i. Technological
      • ii. Customer
      • iii. Vendor

2. AI in Fintech - Market Dynamics

  • 2.1. AI in Fintech
    • 2.1.1. Financial Advice
      • i. AI Disruptor: Case Study - Sentient Technologies
    • 2.1.2. Smart Wallets
      • i. AI Disruptor: Case Study -Wallet.AI
    • 2.1.3. Further Impacted Areas
      • Table 2.2: AI Start-Ups in Finance
    • 2.1.4. Investment Landscape
      • Table 2.3: AI Investment - Finance Sector
    • 2.1.5. AI Outlook in Finance
      • Figure 2.4: Juniper Phased Evolution: AI in Finance
    • 2.1.6. Finance AI Key Takeaways

3. AI Vendor Landscape

  • 3.1. Introduction
  • 3.2. AI Disruptors & Challengers Quadrant
    • 3.2.1. Introduction
    • 3.2.2. Landscape Analysis
    • i. Embryonic Vendors
    • ii. Catalysts
    • iii. Disruptors
    • iv. Nascent
      • Figure 3.1: AI Disruptors & Challengers Quadrant
  • 3.3. Juniper Leaderboard
    • Figure 3.2: Vendor Assessment Criteria
    • 3.3.1. Limitations & Interpretation
  • 3.4. Juniper Leaderboard Scoring Results
    • Table 3.3: Juniper Leaderboard: AI Scoring Table
    • Figure 3.4: Juniper Leaderboard: AI
    • 3.4.1. Stakeholder Groupings
      • i. Established Leaders
      • ii. Leading Challengers
  • 3.5. AI Movers & Shakers
  • 3.6. Vendor Profiles
    • 3.6.1. Alphabet
      • i. Corporate Profile
        • Table 3.5: Alphabet AI Acquisitions (2013 onwards)
        • Table 3.6: Alphabet Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Development Opportunities
    • 3.6.2. Amazon
      • i. Corporate Profile
        • Table 3.7: Amazon AI Acquisitions (2012 onwards)
        • Table 3.8: Amazon: Key Financial Data ($bn) 2013-2014
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partners
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.3. Apple
      • i. Corporate Profile
        • Table 3.9: Apple AI Acquisitions (2012 onwards)
        • Table 3.1O: Apple: Key Financial Data ($bn) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.4. Baidu
      • i. Corporate Profile
        • Table 3.11: Baidu Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.5. Facebook
      • i. Corporate Profile
        • Table 3.12: Facebook AI Acquisitions (2014 onwards)
        • Table 3.13: Facebook Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.6. IBM
      • i. Corporate Profile
        • Table 3.14: IBM AI Acquisitions (2014 onwards)
        • Figure 3.15: IBM Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.7. Intel
      • i. Corporate Profile
        • Table 3.16: Intel AI Acquisitions (2013 onwards)
        • Figure 3.17: Intel Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.8. Microsoft
      • i. Corporate Profile
        • Table 3.18: Microsoft AI Acquisitions
        • Table 3.19: Microsoft Financial Snapshot ($bn) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.9. Nvidia
      • i. Corporate Profile
        • Table 3.20: Nvidia Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.10. Samsung Electronics
      • i. Corporate Profile
        • Table 3.21: Samsung Financial Snapshot ($bn) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products and Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.11. Salesforce.com
      • i. Corporate Profile
        • Table 3.22: Salesforce.com AI Acquisitions (2014onwards)
        • Table 3.23: Salesforce.com Financial Snapshot ($m)2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.6.12. Twitter
      • i. Corporate Profile
        • Table 3.24: Twitter AI Acquisitions (2014 onwards)
        • Table 3.25: Twitter Financial Snapshot ($m) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities

4. AI Fintech - Machine Learning Forecasts

  • 4.1. Introduction
  • 4.2. Methodology & Assumptions
    • Figure 6.1: AI Fintech Methodology
  • 4.3. Total Financial Institution Issued Unsecured Loans
    • Figure 6.2: Annual Unsecured Consumer Loans Issued by Financial Institutions (m), Split by 8 Key Regions 2016-2021
  • 4.4. Unsecured Loans Issued Using Machine Learning Technology
    • Figure & Table 6.3: Total Unsecured Consumer Loans Issued Using Learning Technology (m), Split by 8 Key Regions 2016-2021
  • 4.5. Total Issued Loan Value
    • Figure & Table 6.4: Total Value of Machine Learning Assisted Unsecured Consumer Loans ($m), Split by 8 Key Regions 2016-2021
  • 4.6. Platform Revenues
    • Figure & Table 6.5: Platform Revenues from Unsecured Loans ($m), Split by 8 Key Regions 2016-2021
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