The UK’s banking industry is using AI in many facets of the business.

What’s new: A survey of financial firms in the UK found that nearly two-thirds of respondents have deployed machine learning technology. Many said they expect their use to double in the next two years.

What the report says: The Bank of England and the UK Financial Conduct Authority sent questionnaires to nearly 300 institutions and got responses from a little over 100 firms offering a variety of services.

  • Two thirds of those surveyed are actively using machine learning applications. The median number was two applications per firm.
  • Machine learning is used mostly for fraud detection and anti-money laundering. It also automates customer service in applications such as online chatbots and marketing in tasks like recommending loans or account types.
  • The technology also contributes to risk management including credit lending, trade pricing, insurance pricing, and underwriting.
  • Despite AI’s penetration throughout the industry, few of the firms polled expressed worry about recruiting skilled developers. Instead, they were concerned with overcoming the constraints of legacy IT systems.

Behind the news: AI’s penetration in banking extends well beyond the UK. JPMorgan Chase in its 2018 annual report told investors it had gone “all in on AI.” HSBC recently opened data science innovation labs in Toronto and London to help process insights from the 10 petabytes of data its clients generate each year. Citigroup is using AI to fight fraud, Bank of America has an AI-powered customer service bot, and Capital One says it uses AI from end to end.

Why it matters: Banking and finance tend to fly under the radar in press reports on AI’s role in traditional industries. This report, while specific to the UK, may well correlate with trends in banks around the world.

We’re thinking: The report lists nine classes of ML algorithms used by respondents including trees, clustering, neural networks (used in roughly 32 percent of cases), and reinforcement learning (around 15 percent). The category called Other is used around 35 percent of the time. We’re happy to call, say, linear regression an ML algorithm. Given such an expansive definition, though, we imagine that most financial institutions use machine learning in some capacity.


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