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Artificial Intelligence (AI) in Retail Banking - Thematic Research

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  • 41 Pages
  • June 2018
  • Region: Global
  • GlobalData
  • ID: 4584213
Artificial Intelligence (AI) in Retail Banking - Thematic Research


For six decades machine learning (ML) was poised to take off because members of the ‘artificial intelligentsia’ had already come up with the theoretical models that could make it work. The problem was that they were waiting for rich data sets and affordable ‘accelerated computing’ technology to ignite it.

These are now becoming more available, and amid a swirl of hype, ML - i.e., software that becomes smarter as it trains itself on large amounts of data - has gone mainstream, and within five years its deployment will be essential to the survival of companies of all shapes and sizes across all sectors.

For many investors, ML=AI; ML is an AI technology that allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures.

There are many other AI technologies - from image recognition to natural language processing (NLP), gesture control, context awareness, and predictive APIs - but ML is where most of the investment community’s funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.

Many companies, like Alphabet, have already become ‘AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are getting commoditized by being open sourced and pre-packaged into developer toolkits that anyone can use.


This report is part of our ecosystem of thematic investment research reports, supported by our “thematic engine”. About our Thematic Research Ecosystem:
  • The author has developed a unique thematic methodology for valuing technology, media and telecom companies based on their relative strength in the big investment themes that are impacting their industry. Whilst most investment research is underpinned by backwards looking company valuation models, the author’s thematic methodology identifies which companies are best placed to succeed in a future filled with multiple disruptive threats. To do this, the author tracks the performance of the top 600 technology, media and telecom stocks against the 50 most important themes driving their earnings, generating 30,000 thematic scores. The algorithms in the author’s “thematic engine” help to clearly identify the winners and losers within the TMT sector. Our 600 TMT stocks are categorised into 18 sectors. Each sector scorecard has a thematic screen, a risk screen and a valuation screen. Our thematic research ecosystem has a three-tiered reporting structure: single theme, multi-theme and sector scorecard. This report is a Multi-Theme report, covering all stocks, all sectors and all themes, giving readers a strong sense of how everything fits together and how conflicting themes might interact with one another.

Reasons to buy
  • Our thematic investment research product, supported by our thematic engine, is aimed at senior (C-Suite) executives in the corporate world as well as institutional investors.

  • Corporations: Helps CEOs in all industries understand the disruptive threats to their competitive landscape

  • Investors: Helps fund managers focus their time on the most interesting investment opportunities in global TMT.

  • Our unique differentiator, compared to all our rival thematic research houses, is that our thematic engine has a proven track record of predicting winners and losers.

Table of Contents


  • Definitions

  • History of machine learning

  • How does deep learning work?

  • Technology trends

  • Macro-economic trends

  • Applications of AI in Retail Banking

  • Ten categories of AI software

  • The tech sector’s angle

  • The Webscale companies

  • Enterprise software players

  • Proprietary datasets are also important

  • AI and ML are transforming the chipset market

  • The two critical components of any successful AI engine

  • Recommendations for retail banks

  • How AI vendors can sell into the retail banking sector

  • Recommendations for IT vendors

  • Timeline

  • Market size and growth forecasts

  • Listed tech companies

  • Privately held tech companies

  • Retail banking companies


Companies Mentioned (Partial List)

A selection of companies mentioned in this report includes, but is not limited to:

  • Alphabet
  • Amazon
  • Apple
  • Baidu
  • Cognex
  • Facebook
  • IBM
  • iFlytek
  • Intel
  • Nvidia
  • Salesforce
  • SAP
  • Splunk
  • Darktrace
  • Descartes Labs
  • Mobvoi
  • Palantir
  • Sentient Technologies
  • Vicarious
  • Ayasdi
  • Blue Yonder
  • FinGenius
  • Ipsoft
  • Kabbage
  • Kensho
  • LendUp
  • DataFox
  • MetaMind
  • Numenta
  • Preferred Networks
  • Quid
  • Sentient Tech
  • Uber
  • Viv Labs
  • Zephyr Health
  • BBVA
  • Commonwealth Bank of Australia (CBA)
  • Danske Bank
  • DBS Bank (Digibank)
  • Garanti
  • ICBC
  • La Caixa
  • Nordea
  • N26
  • USAA
  • Wells Fargo