Royal bank of Scotland to rebrand as NatWest amid cuts

Royal Bank of Scotland’s CEO Alison Rose. (REUTERS File Photo)
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Updated 15 February 2020

Royal bank of Scotland to rebrand as NatWest amid cuts

Royal Bank of Scotland’s (RBS) new Chief Executive Alison Rose unveiled a new strategy for the taxpayer-backed bank on Friday, including radically cutting back the size of its loss-making investment bank and renaming the company NatWest.

Rose, who replaced former CEO Ross McEwan in November to become the first woman to lead one of the UK’s major banks, is hoping a rebrand will help rehabilitate the lender’s image after years of scandals following a £45 billion taxpayer rescue during the 2008 financial crisis.

Although the RBS brand will live on in Scotland, the bank will stop using the 293-year-old name at group level and adopt the NatWest brand that grew out of National Westminster Bank, which RBS bought in 2000, and which consistently polls as more popular in customer satisfaction surveys in Britain.

The new strategy and better-than-expected profits were, however, overshadowed by a lower than expected eight pence dividend, sending shares down 6 percent in morning trading and demonstrating the challenge Rose faces to win over investors.

The payout will amount to £1 billion ($1.3 billion) including a £600 million windfall for taxpayers, who still own 62 percent of the bank.

RBS Chairman Howard Davies told reporters the bank’s preference was to use excess capital to buy back stock from the government as and when it restarts selling following the March 11 budget.

Rose’s strategy includes plans to halve investment bank NatWest Markets’ risk weighted assets to £20 billion.

She added that making the bank a greener entity would be a top priority to help tackle “one of the defining issues of our generation,” following similar strategy updates by BP and Blackrock in recent weeks.

RBS will stop financing coal power stations by 2030, and aim to be carbon positive by 2025.

Analysts took a dim view of the update, with KBW saying there was “no end to the building site” at RBS and the outlook was “horrible.”

“We believe investors will be disappointed with capital return,” said Joe Dickerson, an analyst at Jefferies.

The lender reported pre-tax profits of £4.2 billion for 2019, 24 percent higher than 2018 and above analyst expectations.

Results were dented by a loss of £121 million at NatWest Markets and a previously announced £900 million compensation provision for mis-sold insurance, part of a wider industry scandal.


Man vs. machine in bid to beat virus

Updated 20 February 2020

Man vs. machine in bid to beat virus

  • Human and artificial intelligence are racing ahead to detect and control outbreaks of infectious disease

BOSTON: Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments to save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It is difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The first public alert outside China about the novel coronavirus came on Dec. 30 from the automated HealthMap system at Boston Children’s Hospital. At 11:12 p.m. local time, HealthMap sent an alert about unidentified pneumonia cases in the Chinese city of Wuhan. The system, which scans online news and social media reports, ranked the alert’s seriousness as only 3 out of 5. It took days for HealthMap researchers to recognize its importance.

Four hours before the HealthMap notice, New York epidemiologist Marjorie Pollack had already started working on her own public alert, spurred by a growing sense of dread after reading a personal email she received that evening.

“This is being passed around the internet here,” wrote her contact, who linked to a post on the Chinese social media forum Pincong. The post discussed a Wuhan health agency notice and read in part: “Unexplained pneumonia???”

Pollack, deputy editor of the volunteer-led Program for Monitoring Emerging Diseases, known as ProMed, quickly mobilized a team to look into it. ProMed’s more detailed report went out about 30 minutes after the terse HealthMap alert.

Early warning systems that scansocial media, online news articles and government reports for signs of infectious disease outbreaks help inform global agencies such as the World Health Organization — giving international experts a head start when local bureaucratic hurdles and language barriers might otherwise get in the way.

Some systems, including ProMed, rely on human expertise. Others are partly or completely automated.

“These tools can help hold feet to the fire for government agencies,” said John Brownstein, who runs the HealthMap system as chief innovation officer at Boston Children’s Hospital. “It forces people to be more open.”

The last 48 hours of 2019 were a critical time for understanding the new virus and its significance. Earlier on Dec. 30, Wuhan Central Hospital doctor Li Wenliang warned his former classmates about the virus in a social media group — a move that led local authorities to summon him for questioning several hours later.

Li, who died Feb. 7 after contracting the virus, told The New York Times that it would have been better if officials had disclosed information about the epidemic earlier. “There should be more openness and transparency,” he said.

ProMed reports are often incorporated into other outbreak warning systems. including those run by the World Health Organization, the Canadian government and the Toronto startup BlueDot. WHO also pools data from HealthMap and other sources.

Computer systems that scan online reports for information about disease outbreaks rely on natural language processing, the same branch of artificial intelligence that helps answer questions posed to a search engine or digital voice assistant.

But the algorithms can only be as effective as the data they are scouring, said Nita Madhav, CEO of San Francisco-based disease monitoring firm Metabiota, which first
notified its clients about the outbreak in early January.

Madhav said that inconsistency in how different agencies report medical data can stymie algorithms. The text-scanning programs extract keywords from online text, but may fumble when organizations variously report new virus cases, cumulative virus cases, or new cases in a given time interval. The potential for confusion means there is almost always still a person involved in reviewing the data.

“There’s still a bit of human in the loop,” Madhav said.

Andrew Beam, a Harvard University epidemiologist, said that scanning online reports for key words can help reveal trends, but the accuracy depends on the quality of the data. He also notes that these techniques are not so novel.

“There is an art to intelligently scraping web sites,” Beam said. “But it’s also Google’s core technology since the 1990s.”

Google itself started its own Flu Trends service to detect outbreaks in 2008 by looking for patterns in search queries about flu symptoms. Experts criticized it for overestimating flu prevalence. Google shut down the website in 2015 and handed its technology to nonprofit organizations such as HealthMap to use Google data to build their own models.

Google is now working with Brownstein’s team on a similar web-based approach for tracking the geographical spread of the tick-borne Lyme disease.

Scientists are also using big data to model possible routes of early disease transmission.