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Bringing AI into the Enterprise: A Machine Learning Primer

出版商 Mercator Advisory Group, Inc. 商品編碼 545435
出版日期 內容資訊 英文 41 Pages
商品交期: 最快1-2個工作天內
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將AI引入銀行:機器學習指南 Bringing AI into the Enterprise: A Machine Learning Primer
出版日期: 2017年08月21日 內容資訊: 英文 41 Pages



  • 機器學習:像軟體一樣,機器學習可以所有商務領域廣泛的問題
    • 技術可以以防禦性的方式用於降低成本
    • 了解如何使用機器學習在進攻中擴大商機是非常重要的
  • 機器學習:現在用於大幅改善詐欺檢測同時減少誤報
    • 改變了消費者行為的預測方法
    • 行動生物識別技術根本上地破壞了傳統認證市場
  • 最重要的一點:機器學習通過啟用自然語言界面自然地語言介面,實現上下文商業和自動化代理
    • 機器學習改變了消費者的智慧型手機和與服務供應商的互動
  • Mercator Advisory Group:建議掌握機器學習最具最生產性的正確想法
    • 軟體學者:特定領域 (非法交易·精神狀態檢測·物體發現·影像內臉部辨識等)中進行資料·信號分析·影響的軟體系統
  • 雲端基礎設施:預計將是機器學習佔盡競爭優勢的領域
    • 因為建立·訓練·引進·管理一個新的通用平台,需要全新的基礎設施


  • Amazon
  • Cisco
  • Clinc
  • Facebook
  • FIS
  • Google
  • IBM
  • Microsoft
  • OpenAI
  • Oracle
  • Salesforce
  • Slack
  • Twilio
  • Unit 4
  • USAA

AI's impact on banking will be broader and faster than the impact of the internet.

New research from Mercator Advisory Group shows how machine learning, a.k.a. AI, has changed consumer behavior and expectations and will evolve to alter all aspects of bank operations.

A new research report from Mercator Advisory Group titled Bringing AI into the Enterprise: A Machine Learning Primer provides an analysis of the impact machine learning will have on bank operations and payments and how it is already shifting consumer behavior. Consumers increasingly expect their smartphone will answer their questions, give them directions, and warn them when accidents will slow them down. Over time, machine learning will become as prevalent within banks as software systems are today. Eventually every software application will be reconstructed to accommodate machine learning - it's simply a matter of time.

This report provides an analysis of the current state of machine learning with a deep dive into existing technologies and breakthroughs that represent new deployment opportunities, such as deep learning, adversarial networks, and transfer learning. The report identifies the incredible breadth of business processes that are impacted by machine learning and recommends areas that should be targeted first. It recommends an approach to enterprise deployment and identifies the important differences between deploying a machine learning solution and deploying traditional software and provides recommendations that will prevent silos of machine learning that would limit the ability of machine learning tools to collaborate.

"The impact of machine learning on the enterprise is breathtaking. It is lowering costs, creating amazing new market opportunities for those willing to innovate, and altering the ways in which consumers behave. As consumers become familiar with an environment that responds to their needs, they will increasingly expect their service providers (including their financial services providers) to become more proactive. Authentication and fraud management have already been affected by machine learning and can save the institution several basis points in fraud costs. But financial institutions should evaluate the impact of machine learning much more broadly," commented Tim Sloane, Vice President of Payments Innovation and Director of Mercator Advisory Group's Emerging Technology Advisory Service, who is the author of the report. "It took software decades to escape the water-cooled computer room, but the evolution of machine learning will be much faster. Mobile phones and cloud computing will enable machine learning to impact a much broader range of processes in a much shorter time, and even computing hardware and the cloud itself will feel the impact."

This report is 41 pages long and has 13 exhibits.

Companies mentioned are: Amazon, Cisco, Clinc, Facebook, FIS, Google, IBM, Microsoft, OpenAI, Oracle, Salesforce, Slack, Twilio, Unit 4, USAA, and

One of the exhibits included in this report:


Highlights of the report include:

  • Like software, machine learning can be applied to an extremely wide range of specific problems across all business domains. Although the technology can be used in a defensive fashion to lower costs, understanding how to use machine learning in offense to expand business opportunities is far more important.
  • Machine learning is used today to greatly improve fraud detection while simultaneously reducing false positives, it has changed how consumer behavior is predicted, and it is fundamental to behavioral biometrics, which is disrupting the traditional authentication market.
  • Perhaps most important, machine learning has changed how consumers interact with their smartphone and service providers by enabling natural language interfaces, contextual commerce, and automated agents.
  • Mercator Advisory Group suggests that the most productive and accurate way to think of machine learning is as a software savant that is "a software system designed specifically to analyze and act on data and signals within a specialized domain (as in transactional fraud, detection of emotional state, or discovering objects or faces in a picture)."
  • The cloud infrastructure will be a battleground for machine learning dominance, demanding an entirely new infrastructure for building, training, deploying, and managing these new general-purpose platforms.
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