Banks discreetly seek personnel to mine alt data riches

Citi, Credit Suisse, HSBC and Morgan Stanley are hiring data scientists for a plethora of new initiatives

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The world’s largest banks are hiring data scientists to harvest information that can be sold to clients, or used in improving their own operations.

A job post on Citi’s website seeks data scientists to join a newly minted consulting team that will work directly with clients on bespoke data requests. The projects will involve what the bank calls “non-markets” data – foot traffic, cellphone signals, satellite images, credit card and point-of-sale transactions data, geospatial datasets and, notably, “institutional” aggregated financial flows.

The consulting group would also handle individual project requests, familiarising clients with the range of data available and providing support to test any datasets.

Citi already provides its internal markets data to institutional clients through its Citi Velocity platform at no cost. The bank declined to comment on its data science efforts.

Citi is not alone. Goldman Sachs is building a team to sell alternative data, while State Street has an entire division – State Street Global Exchange – dedicated to turning the data it derives from servicing roughly 11% of global fund assets into useful insights.

So far, State Street has secured its clients’ permission to anonymise their data and create products based on it. For instance, the bank got consent from its private equity customers to create a benchmark index based on several thousand funds.

These types of services are currently offered free of charge to customers, but that is likely to change. “We’re looking to do more of those things with an explicit consent framework going forward,” says JR Lowry, North American head of State Street Global Exchange, of paid products it plans to bring to market.

Others are bringing in specialists to create alternative datasets for internal use.

Credit Suisse is currently hiring a data scientist to build a central, cloud-based data repository of all equities trading flows to support streaming analytics, algo backtesting, monitoring and regulatory reporting.

HSBC is looking for a data officer to join a company-wide analytics program, with an initial focus on client data.

And Morgan Stanley is seeking data scientists with machine learning backgrounds to help sales and marketing teams with things like sales prospecting, segmentation and lead generation.

Monetising customer data is hardly new. Retailers and others have a long history of selling their customer lists
Robert Frey, FQS Capital Partners

As Risk.net previously reported, banks are talking privately to asset managers about building data and analytics products based on data generated from their own business lines and selling them back to clients.

Citi, Credit Suisse, Morgan Stanley and UBS were explicitly cited by fund managers as exploring the space, alongside Goldman Sachs and JP Morgan, but none of the banks would confirm it.

Their “help wanted” posts, however, show the race is on to extract value from their own institutional data, both in terms of selling that data back to clients as analytics, or improving other areas of their business.

“Monetising customer data is hardly new. Retailers and others have a long history of selling their customer lists,” says Robert Frey, chief executive of quant fund FQS Capital Partners.

For investment banks, though, client privacy is a serious concern.

“Aside from the important privacy aspects, a broader question of ownership arises. To whom does this data belong? Should the consumer be rewarded in some way for what is clearly a valuable asset?” he asks. “One may argue that free services have a slightly stronger case, but the purchasers of retail products are paying for a service already.”

The combination of growing adoption of data science and machine learning in asset management, with the commoditisation of processing power and storage, has opened the door for new datasets to be mined for alpha. That includes data generated within investment banks such as flow data, client portfolio positioning and derivatives pricing models.

Although not an apples-to-apples comparison, revenues from the data businesses of the world’s biggest exchanges grew 9% to $5.9 billion in 2017, with data accounting for one-fifth of total exchange industry revenues, according to market research firm Burton-Taylor International Consulting.

Total buy-side spending on alternative data is projected to reach $1.7 billion by 2020 from $656 million this year. The number of employees who work directly with it – data analysts, scientists and engineers – has quadrupled to 340 since 2012, according to AlternativeData.org, an organisation that tracks the business.

Most asset managers agree that the real prize at banks is their flows data, which can give funds a better view of where the market is positioned, even if the data is aggregated and anonymised to avoid breaches of client confidentially.

Banks that harvest their internal data also have the advantage over data vendors, which have to consolidate data from exchanges and sell-side providers, and then develop and sell analytics on it.

“The industry has been pooling its data and putting vendors like Bloomberg and Thomson Reuters in a position to create a business model out of it for many, many years at this point,” a bank executive says. “What’s happening now is, it’s the next version of those services and people are becoming more aggressive or open-minded about how to monetise that data.”

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