Machine Learning/Finance

From Thalesians Wiki

Machine Learning

The importance of machine learning in finance continues to grow.

Developers now make up a quarter of Goldman Sachs' workforce

Jia Jen Low. T_HQ. 14 February 2020.

Abstract: "The leading finance firm says it's now competing with Silicon Valley tech giants for talent."

With the emergence of technologies such as artificial intelligence (AI), blockchain, machine learning (ML), and big data, finance is one of the most disrupted sectors. The innovations mushrooming from the effect are digital wallets, chatbots with financial knowledge, and smart contracts.

The finance sector is one of the keenest investors in emerging technology. Just shy of two-thirds (64 percent) of financial services leaders expect to be mass AI adopters within the next two years. The industry is also ahead of others with blockchain—enabling faster processing and quicker settlement of trades.

However, these digital innovations require teams of developers, data scientists, and tech specialists.

At Goldman Sachs' Technology and Internet Conference in San Francisco, the Wall Street giant's co-Chief Investment Officer, George Lee, explained how the firm is on a tech hiring spree in a bid to rapidly expand its engineering talent. The investment bank currently staffs 10,000 developers, making up a quarter of its total workforce.

Last year, the leading financial firm celebrated a memorable 150th birthday announcing fourth-quarter profit that exceeded expectations by US$1.59 per share. Now the investment bank is turning up the knob which means it must go head-to-head with tech giants—such as Amazon, Microsoft, and Google—for future talent.

"The trend is very much toward technology companies and we need to compete at that level," said Lee.

Last year, Goldman achieved several milestones by releasing new products like the Apple Card, the credit card it launched with the iPhone maker last year. It has also made headway into automated support, introducing a robo advisor to help with consulting small clients.

But the firm is keenly aware that the finance sector is at continued risk of disruption, with new fintechs reimagining customer experience, delivering enhanced services and new security solutions.

Lee explained the road to hiring top engineers required some shifts in the firm's rigid organizational structure. Wanting to leverage software development's "open-source" approach, a more distributed workforce is being adopted, with developers working from cities across the globe—some of the key locations include India, Poland, and Dallas (the US).

This has been a challenge for the Goldman Sachs' strict working operations—Lee said the firm had to navigate and facilitate these preferences while remaining mindful of company policies.

As part of the aggressive hiring strategy, the bank is tapping into the graduate talent pool by focusing on recruiting efforts at college campuses, but it isn't the only global financial firm that is hungry for tech talents, with JPMorgan, Bank of America and Citi also getting their fair share of digitally skilled workforce.

Finance is headed for AI mass adoption—and soon

Jia Jen Low. T_HQ. 6 February 2020.

Abstract: "While other industries struggle with AI, finance members are locked in an arms race.

Despite the hype, many organizations are facing an AI "reality check" this year. Everyone is buying into the technology's promise, but difficulties in implementation are leading many firms to roll back plans.

The finance sector might be the exception to the rule though. According to a new report by the World Economic Forum (WEF) and Cambridge Centre for Alternative Finance (CCAF), organizations here are confident they are already reaping the advantages.

Just shy of two-thirds (64 percent) of financial services leaders expect to be mass AI adopters within the next two years, exploding from just 16 percent today. Aside from cost reduction, applications span revenue generation, process automation, risk management, customer service, and client acquisition.

More than 150 industry leaders from both fintech and incumbent financial institutions took part in the report, Transforming Paradigms: Global AI in Financial Services Survey. Findings painted a picture of an industry already well ahead with the technology. But they also highlighted a distinction between the use of AI by the new wave of fintech disruptors and industry incumbents.

A majority of fintech firms are developing AI-powered products and services, with the aim of automating decition-making and offering more variety in cloud solutions. Legacy firms, meanwhile, are using AI to strengthen financial services and systems, and expect their employment rates to drop by 9 percent within the next 10 years as a result of automation.

According to research by Accenture, banks adopting AI expect the technology to help cut IT operations costs by between 20-25 percent. As more operations are automated, AI will also allow bank employees to spend more time on "exceptional work", or the 20 percent of non-routine tasks that drive 80 percent of value creation.

From a customer experience (CX) standpoint, AI could greatly enhance products. Predictive analytics could track spending patterns and help banks set credit limits; real-time sentiment analytics could provide customer support cues, while AI can also be used to identify fraudulent activities. TechHQ recently spoke to Revolut on how it uses machine learning to tackle FinCrime.

While other industries struggle to get to grips with AI and machine learning technology, particularly in discovering viable applications, the finance industry is already flexing its AI muscle. Indeed, 77 percent of respondents are anticipating AI to have significant importance in their businesses within two years.

With so many use cases for the technology—in improving products, experience, and internal operations—there's plenty of room to innovate. But the growing digitization of the industry opens doors for further disruption: nearly half of respondents saw a new and significant competitive threat emerging in tech firms.

On the study's findings, WEF Head of Financial and Monetary Systems Matthew Blake, said: "The comprehensive and global study confirms that AI is affecting the financial system at an accelerating pace."

"With the rising trend of mass adoption of the technologies throughout financial services, those firms that implement AI quickly look set to sprint ahead."

Rival banks are hiring technologists from Goldman Sachs

Sarah Butcher. eFinancialCareers. 27 January 2020.

Goldman Sachs might want to keep a tighter grip on its technology talent. Since the start of this year, various of its senior technologists have found new jobs elsewhere.

The latest big name to move is Gavin Leo Rhynie, the former head of platform technology at Goldman in New York City, who has just joined JPMorgan as head of engineering and architecture for the corporate and investment bank (CIB) according to a memo sent by CIB technology head Mike Grimaldi. Leo Rhynie isn't JPM's only ex-Goldman hire though: JP also just poached James Kirby, a London-based vice president who spent seven years at Goldman with a focus on enterprise architecture and technology implementation.

Morgan Stanley has been checking out Goldman's talent too. As we reported earlier this month, the U.S. bank hired Michael Ballard, a VP in digital product at Goldman in New York who joined as an executive director in product strategy.

The exits come as Goldman itself ramps up technology hiring while preparing to cut costs by moving as many as half its technology jobs outside of London. Goldman's technology business is in a state of flux after the departure of leaders like Marty Chavez and Elisha Wiesel last year. However, Goldman technologists told us last week that they're super happy working for the bank, which is less political and pressured than big tech firms, gives them plenty of flexibility and is a better environment than most other places in finance.

Banks are big spenders on technology with JPMorgan, Bank of America and Citi spending the most. Citi is also hiring senior technologists externally: the bank just recruited James Linnett as CIO of global functions technology from Bank of America, where he spent 18 years.

Not all the new technology hires have technology backgrounds. JPMorgan is setting up a new London machine learning centre run by Chak Wong, a former trader and structurer at SocGen, Barclays, Morgan Stanley, Goldman and UBS. Wong, too, is hiring associates. Goldman especially may want to keep a strong grip on its machine learning people in the City.

A Global AI in Financial Services Survey

Lukas Ryll, Mary Emma Barton, Bryan Zheng Zhang, Jesse McWaters, Emmanuel Schizas, Rui Hao, Keith Bear, Massimo Preziuso, Elizabeth Sege, Robert Wardrop, Raghavendra Rau, Pradeep Debata, Philip Rowan, Nicola Adams, Mia Gray, Nikos Yerolemou.

Goldman Sachs hunts AI experts for all-important quant team

Paul Clarke. Financial News. Friday November 23, 2018 4:29 am.

Abstract: "US bank is building its vast strats department by hiring a new generation of machine learning and artificial intelligence specialists."

Goldman Sachs is pitting itself against tech giants like Google and Facebook to attract scarce artificial intelligence talent as it places a renewed emphasis on ensuring its sales and trading staff are given cutting-edge IT.

The US investment bank, which has been described by CEO Lloyd Blankfein as a "technology company", is building its strats department, a huge division comprised of quant and technology professionals and which serves its trading businesses among other functions.

Thalia Chryssikou, co-head of global sales strats and structuring across fixed income currencies and commodities and equities, said the bank has shifted its focus towards recruiting a "new generation" of strats.

"The strats we hired 10 to 15 years ago typically specialized in modeling risk and pricing analytics," she said during an interview in an email newsletter from the bank. "Today, we're focused on hiring a new generation of strats who specialize in data management and analytics, including machine learning (ML), artificial intelligence (AI), program management and digital product design, in addition to quantitative sciences."

Goldman's strats division is made up of quantitative finance, engineering and technology professionals spanning various departments across the bank's trading functions, as well as back office and compliance. It has been growing since then-chief information officer Marty Chavez was handed control of both technology and strats in 2014.

Strats now comprise 27% of total headcount within Goldman Sachs's securities business, according to Chryssikou, up from 18% five years ago. Strats made up 27% of experienced hires within Goldman's FICC division last year, according to a September presentation by chief operating officer Harvey Schwartz. The team has also doubled within its investment bank since 2014.

Strats have become increasingly important to a range of functions at Goldman Sachs as it looks to automate more processes and equip its markets business with cutting-edge technology. Last year, for example, a team of 75 programmers at the bank introduced software to reduce some of the grunt work of junior investment bankers.

More broadly, however, Goldman's strats and technology teams have been creating systems to help their sales and trading staff make more informed decisions for their clients. In its fourth quarter results presentation, Chavez said the real growth in headcount last year was within "engineering" as the bank is "digitising our plarform generally".

Goldman recently hired Jeff Wecker as chief data officer and Matthew Rothman as head of data and client service. Rothman is currently hiring quantitative researchers within the securities division at Goldman.

"Almost nothing we do to service our clients—from trade execution, regulatory compliance and the advanced quantitative analysis we touched on earlier—could be possible without investments in technology and engineering," said Chryssikou. "It is essential and our hiring in the business reflects that."


Growth in fixed income futures algorithmic trading at JP Morgan has accelerated rapidly in 2020 as buy-side traders globally turned to the investment bank's machine learning-equipped algos to grapple with intense market volatility.

Speaking to The TRADE, Peter Ward, global head of futures and options electronic execution at JP Morgan, explains that while the volatility contributed to recent growth, adoption of futures algo trading has picked up pace with clients significantly in the last few years.

Since 2016, futures volumes traded via algos at JP Morgan has increased 40% year-on-year. In fact, algos now comprise almost 20% of the bank's total futures trading flow, up significantly from roughly 4-5% in 2016 and 2017, figures seen by The TRADE have revealed.

The period of intense volatility in 2020 due to the global pandemic played a key role in the cumulative buy-side adoption of futures algos as traders became more accustomed to on-screen execution and liquidity.

Reinforcement Learning

Quantum Computing