Saudi investment fund PIF ‘has $300bn in assets and counting’

Saudi Arabia’s Vision 2030 reform plan is expected to transform the country’s key wealth fund into one of the world’s largest sovereign investment vehicles. (Shutterstock)
Updated 10 June 2019

Saudi investment fund PIF ‘has $300bn in assets and counting’

  • Boost in Kingdom’s wealth fund ‘will improve country’s international investment position,’ study shows

LONDON: Saudi Arabia’s key wealth fund has about $300 billion in assets and its growing size is set to “improve the country’s international investment position,” a new report has found.
Roughly a quarter of the Kingdom’s Public Investment Fund (PIF) holdings are overseas, with investments in companies like electric car maker Tesla and SoftBank’s Vision Fund, according to the Institute of International Finance (IIF) analysis. 
A raft of privatization deals and the planned $69 billion sale of a controlling stake in petrochemicals giant Saudi Basic Industries (SABIC) to Saudi Aramco is set to further boost the fund’s coffers, according to the IIF.
That means it is likely PIF will hit a target of $400 billion in assets by 2020, something the fund’s representatives have previously suggested is on track. 
“The expected further increase in the PIF’s assets abroad will improve the country’s international investment position,” the IIF report said.
“We now estimate PIF’s assets at about $300 billion, of which one-fourth are invested abroad, including in … Blackstone’s infrastructure fund, Egypt’s investment fund, Russia’s investment fund, and Uber. Proceeds from privatization (a target of about
$200 billion) and the eventual 5 percent sale of Aramco (a target of $100 billion) will further boost the PIF’s assets.”
However, the IIF noted that the privatization drive has been delayed due to legal impediments, concerns about implications for the labor market, and — in the case of the planned sale of a 5 percent stake in Saudi Aramco — regulatory procedures that need to be addressed.
The Vision 2030 reform plan envisions the transformation of the PIF into one of the world’s largest sovereign investment vehicles, managing $2 trillion by 2030. 
The Sovereign Wealth Fund Institute estimates PIF’s current assets at $320 billion, higher than the IIF’s assessment, making the Saudi entity the 10th largest fund of its type globally. Representatives of PIF did not immediately respond to a request for comment. 
The IIF report also found that Saudi Arabia’s holdings of US government bonds climbed to a peak of $170 billion in March 2019. The Kingdom has also “repositioned” its assets from euro and UK pounds to US dollars, the institute said.
“The increase in the Saudi appetite for US bonds coincided with relatively higher US yields and unfavorable investment sentiment in (emerging markets) and the euro zone,” the report noted.


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.