Machine Learning/Finance

From Thalesians Wiki

The importance of machine learning in finance continues to grow. This page attempts to collect the most important developments in this area as mentioned in the Press.

2021.07.09 China Targets AI Dominance by 2030

Bloomberg Quicktake. July 9th, 2021, 9:48 AM GMT+0100.

Endless cups of coffee, hi-tech tools to help doctors, a masseuse who never gets tired... Just some of the latest innovations on display at this year's World Artificial Intelligence Conference in Shanghai.

"The advantages are first of all its trajectory is a digital record of master-level massage skills. Compared with ordinary masseurs, its techniques are more accurate and precise. Second, with the 3D images it can find different acupuncture points according to different people's body shapes and can massage in a targeted manner."

Meet Walker X, he could be your next butler.

"The functions that we have been able to achieve in the family setting include bringing you a Coke from the refrigerator, opening a drink, bringing you a newspaper, fetching an umbrella, and watering the flowers, playing chess with you, singing and doing homework with you. It can voice chat and has other emotional interactions. In the future, we will make robots that have a more diversified role in the entire home. Walker will play the role to help the family, accompany you as you grow up and accompany children and the elderly. It can be a teacher of the family as well as a helper who can do a variety of housework in the family.

AI is also helping drive medical progress. Morphogo is a bone marrow cytomorphology analysis system. It can make a diagnosis within 1 day compared to the usual 5 to 10 days with traditional hospital methods.

Yang Chuhu, ZhiWei Information Technology Co. Ltd.: "This kind of high-efficiency detection is crucial for accurate diagnosis and timely treatment which cannot be achieved only by manual work. Taking China for example, China has vast remote areas and it is impossible for people living in remote villages to go to a big hospital for a rapid morphological examination of bone marrow. Without such a system, it can be said that the life and health of patients with blood diseases in remote areas cannot be effectively guaranteed. This is not a problem for China alone. This is a global problem."

China has set its sights on building a domestic AI industry worth nearly $150 billion in the next few years and of becoming the leading AI power by 2030.

2021.04.30 Goldman Sachs is betting on Artificial Intelligence to drive growth

Disha Sinha. Analytics Insight. April 30, 2021.

Artificial Intelligence (AI) has taken a major role in acting as the main driver of upcoming hi-tech future in the world. It is shifting the information age to a completely new digital domain where upgraded machines help to solve critical decisions and assist in diverse sectors of a country. The banking and financial sector is very keen in investing in AI to protect their customers against the competitors in this market. Thus depending on the current scenario, Goldman Sachs, a leading American multinational investment bank, is betting on AI to drive growth in the economy. It has a fund of $72.5 million exclusively for the investment in Artificial Intelligence algorithms and data analytics.

There is a sudden surge in the cases against cyber-security, cyber-threats, phishing, spamming, hacking and many more unethical behaviours from dark web. It is due to the digital transformation in online banking through websites or mobile apps. Goldman Sachs deals with investment management, securities, asset management, prime brokerage and securities underwriting. This means banks need the best security possible against the online threats. Here comes AI to assist Goldman Sachs in the best possible way.

Why Goldman Sachs is betting on AI to drive growth?

  1. The constant fear of cyber-attacks has been reduced with the help of filtering application of AI. Yes, there are possibilities of receiving fraud applications with unethical motives. AI cybersecurity detects and blocks these applications while collecting real-time data from the users on a large scale.
  2. AI-enabled Investment Trust has already become a successful test for Goldman Sachs in enabling the best investment options for the customers. This has improved customer engagement while running through various kinds of analyst reports and news reports. The investment trust runs on NLP (Natural Language Processing) to assist asset managers in identifying undervalued shares and probable opportunities for profits.
  3. Partnership with an AI-based startup,, helps to focus on deep machine learning transparency and model interpretability to predict a better future. It assists in decision-making process for finance department and equity trading floor such as market making, providing liquidity to the bank and many more.
  4. This is the best opportunity to increase growth in hyper-personalised banking through conversational AI. AI will help in 24*7 two-way communication with innumerable personalised responses and feedbacks to the users.

Goldman Sachs prefers to focus on the net profit over the investment cost of the AI. The bank is eager to maintain its brand image through efficient customer experience, upgraded security and service enhancement.

2021.04.08 Machine learning futures algo trading surges at JP Morgan

Hayley McDowell. The TRADE. April 8, 2021 10:02 AM GMT.

Abstract: Peter Ward, global head of futures and options electronic execution at JP Morgan, tells Hayley McDowell that buy-side adoption of its reinfocement learning FICC futures algorithms has surged in recent years, accelerated by the market volatility in 2020.

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 of 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.

"When liquidity is harder to source and there is more volatility, execution performance becomes challenged," Ward explains. "Clients are driven to look at the problem areas in executions and that's when we consult with them to figure out ways to bring in that performance. Maybe they should consider trading at higher volume at the open or close, or perhaps sitting out the first five minutes on the cash open because of the noise. All of that we can customise for them.

"I think the more challenges clients see in execution, the more opportunity there is for us to come in and help them, and the solution is increasingly the customised algorithm."

Customised algorithms have become particularly popular with traders in 2020 and in recent years. Volumes on customised algos at JP Morgan have roughly tripled in each of the past three years, alongside a 21% increase in the number of custom algos in 2020 to almost 50 customisations, up from close to zero in 2017.

The bank's flagship liquidity-seeking algorithm, known as Aqua, is the most common foundation for modified client parameters and customisations. A classic example of customisation is where a client wants to follow a particular trading pattern but then switch urgency or strategy based upon predefined triggers.

JP Morgan rebuilt its algo platform around five years ago to provide the buy-side with more choice about the parameters they can set on their side for algorithms, and there are further customisations that the bank's electronic traders can configure on behalf of clients. Ward adds this has allowed his team to have a "richer" dialogue with clients and demand is clearly there.

"There has always been demand for customised algos, even 10 years ago there was a lot of demand," he says. "We just didn't have a scalable way back then to adapt an algo to what a client really wanted. The reason for that is when a client wants something different, we needed developers to code that and then release it for implementation in the client's platform, which takes a lot of time."

Reinforcement learning

The Aqua algorithm has been a particular area of focus for JP Morgan recently. It uses a technology referred to as reinforcement learning to create advanced signals on order routing and placement.

With reinforcement learning, which is a form of machine learning, the algorithm essentially learns from itself over time by looking back at previous signals that it has generated and evaluates performance. The signals will dictate whether the algo crosses the market or stays passive.

Reinforcement learning technology was first applied to a recently launched model of Aqua that is focused on navigating quarterly roll dates when futures contracts expire. It can be a high-volume period and volatile time for traders as everybody is typically rolling in the same week to the next expiration date. In recent years, this activity has evolved from manual, voice-based trading to more electronic, low-touch trading.

"Previously, a lot of this business was executed through voice desks and one reason for that was because trading systems out there couldn't handle multi-leg products," he says. "As those systems have been developed in the last few years, we found more of that activity moving to electronic channels.

"A lot of volume goes through on calendar rolls and the challenge is around optimising that experience for the clients rather than imposing a model of trading without looking at the particular client objective."

In response to the trend and client demand, JP Morgan developed a model of its Aqua strategy, known as the Roll Algo, which went live not long ago for the most recent US treasury roll in February. It has been especially popular with buy-side traders, according to Ward.

"The Roll Algo model focuses on maximising liquidity and pricing opportunities by using signals that help it understand when to cross the spread. It's the most important area we are working on and has peaked the greatest interest from clients.

"It performed really well in February and there was a lot of client use in that period. With that, the algo learned a lot along the way so we can expect the performance in the next quarter's roll to be improved."

The Roll Algo is not the only new addition to JP Morgan's new strategy line-up. Advanced strategies like Target to trade around the cash or futures close, Multi Leg Strategy for trading multiple instruments at the same time across futures and US treasuries, and options algos have also been developed by the bank.

Volumes in options on futures surged in 2020 as trading floors at major derivatives exchanges like CME that facilitate options trading were forced to shut down. As a result, liquidity shifted to low-touch and electronic channels and JP Morgan's clients began to ask more questions about trading options through algorithms.

"Options on futures volumes have seen significant growth in the industry over the past few years and 2020 was a breakout year for liquidity on-screen," Ward adds.

"With that said there are still challenges and nuances to trading them and that's where we see opportunities to innovate and help our clients with their execution. This can be through simpler Peg and Cross type strategies and ultimately more targeted strategies using a delta or volatility reference."

JP Morgan expects buy-side adoption of futures algo trading to continue increasing in the near future, having been driven by ongoing market developments and trends over the past few years.

Explicit regulatory requirements on best execution and growing appetite among the buy-side to address challenges in futures and options market structure have been instrumental in the growth of this trend. Best execution essentially forces traders to establish benchmarks to measure performance and trading through algorithms can provide an effective way to do this.

New products have also entered the market where liquidity is shared on multiple markets, which presents challenges in trading those products. Nifty derivatives, for example, are now tradeable in both Singapore and India after the Singapore Exchange (SGX) and India's National Stock Exchange ended a two-year dispute which put SGX's futures into question.

Other developments such as extended hours in futures markets also means there are now more hours to trade what is often the same amount of volume. Add periods of decreased liquidity and increased volatility to the mix, traders have progressively sought algorithmic strategies and automated solutions for consistent execution in volatile products, and when targeting cash settlement periods, for example.

It's not just JP Morgan that is doubling down on efforts in futures algo trading. In January, rival investment bank Citi rolled out a suite of execution algorithms, including its flagship Arrival strategy, for futures markets across all major exchanges in the US, Europe, and Asia Pacific.

In contrast to JP Morgan, the electronic traders at Citi handle all of the algo customisations on behalf of clients. Head of EMEA futures electronic execution at Citi, Gordon Ball, said at the time clients don't want to enter numerous parameters to execute an order. He added: "the complexity of operating an intelligent algorithm and fine-tuning customisations sits with us, so our clients can focus on their overall investment and trading objectives".

Elsewhere, a start-up founded by former global head of trading at AQR Capital Management, Hitesh Mittal, launched its own suite of execution algorithms in early 2020 that aims to reduce costs for the buy-side with customised and high-performance strategies. In December, BestEx Research secured $5 million in funding as it prepares to roll out its algos in futures markets.

Amid the arms race in this space, JP Morgan's Ward predicts the pace of fixed income futures algo trading adoption, particularly customised algos, will continue apace in 2021. It remains a significant focus at JP Morgan as different buy-side clients are also now using algorithms to trade futures.

In the past few years, the type of buy-side client seeking algorithmic execution has shifted from being a relatively small number of large hedge fund clients to the more traditional managers, including pension funds, asset managers and insurance companies.

"Five years ago, there were pockets of interest in executing this way, depending on the specific trader or firm's appetite. It's now become far more mainstream, driven by broader electronification in fixed income markets as well more investment firms adopting more explicit execution benchmarks," Ward concludes.

2020.02.14 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.

2020.02.06 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."

2020.01.27 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.

2020.01m 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. University of Cambridge Judge Business School. January 2020.

Abstract: This report presents the findings of a global survey on AI in Financial Services jointly conducted by the Cambridge Centre for Alternative Finance (CCAF) at the University of Cambridge Judge Business School and the World Economic Forum in Q2-Q3 2019. Representing one of the largest global empirical studies on AI in Financial Services, a total of 151 respondents from 33 countries participated in the survey, including both FinTechs (54 per cent of the sample) and incumbent financial institutions (46 per cent of the sample). The study was supported by EY and Invesco.

Highlights from the report

The key findings of this empirical study are as follows:

  • AI is expected to turn into an essential business driver across the financial services industry in the short run, with 77 per cent of all respondents anticipating AI to possess high or very high overall importance to their businesses within two years. While AI is currently perceived to have reached a higher strategic relevance to FinTechs, Incumbents are aspiring to catch up within two years.
  • The rising importance of AI is accompanied by the increasingly broad adoption of AI across key business functions. Approximately 64 per cent of surveyed respondents anticipate employing AI in all of the following categories—generating new revenue potential through new products and processes, process automation, risk management, customer service and client acquisition—within the next two years. Only 16 per cent of respondents currently employ AI in all of these areas.
  • Risk management is the usage domain with the highest current AI implementation rates (56 per cent), followed by the generation of new revenue potential through new AI-enabled products and processes, adopted by 52 per cent. However, firms expect the latter to become the most important usage area within two years.
  • AI is expected to become a key lever of success for specific financial services sectors. For example, it is expected to turn into a major driver of investment returns for asset managers. Lenders widely expect to profit from leveraging AI in AI-enabled credit analytics, while payment providers anticipate expanding their AI usage profile towards harnessing AI for customer service and risk management.
  • With the race to AI leadership, the technological gap between high and low spenders is widening as high spenders plan to further increase their R&D investments. These spending ambitions appear to be driven by more-than-linear increases in pay-offs from investing in AI, which are shown to come into effect once AI investment has reached a "critical" mass of approximately 10 per cent R&D expenditure.
  • FinTechs appear to be using AI differently compared to Incumbents. A higher share of FinTechs tends to create AI-based products and services, employ autonomous decision-making systems, and rely on cloud-based offerings. Incumbents predominantly focus on harnessing AI to improve existing products. This might explain why AI appears to have a higher positive impact on FinTechs' profitability, with 30 per cent indicating a significant AI-induced increase in profitability compared to seven per cent of Incumbents.
  • FinTechs are more widely selling AI-enabled products as a service. Successful real-world implementations demonstrate that selling AI as a service may allow large organisations to create "AI flywheels"—self-enforcing virtuous circles—through offering improved AI-driven services based on larger and more diverse datasets and attracting talent.
  • AI Leaders generally build dedicated corporate resources for AI implementation and oversight—mainly a data analytics function—to work with their existing IT department. On average, they also use more sophisticated technology to empower more complex AI use cases.
  • Leveraging alternative datasets to generate novel insights is a key part of harnessing the benefits of AI with 60 per cent of all respondents utilising new or alternative forms of data in AI applications. The most frequently used alternative data sources include social media, data from payment providers, and geo-location data.
  • Incumbents expect AI to replace nearly nine per cent of all jobs in their organisation by 2030 while FinTechs anticipate AI to expand their workforce by 19 per cent. Within the surveyed sample, this implies an estimated net reduction of approximately 336,000 jobs in Incumbents and an increase of 37,700 jobs in FinTechs. Reductions are expected to be highest in investment management, with participants anticipating a net decrease of 10 per cent within five years and 24 per cent within 10 years.
  • Regardless of sectors and entity types, quality of and access to data and access to talent are considered to be major obstacles to implementing AI. Each of these factors is perceived to be a hurdle by more than 80 per cent of all respondents, whereas aspects like the cost of hardware/software, market uncertainty, and technological maturity appear to represent lesser hindrances.
  • Almost 40 per cent of all respondents feel that regulation hinders their implementation of AI, whereas just over 30 per cent perceive that regulation facilitates or enables it. Organisations feel most impeded by data sharing regulations between jurisdictions and entities, but many also deem regulatory complexity and uncertainty to be burdensome. Firms' assessments of the impact of regulation tend to be more positive in China than in the US, the UK, or mainland Europe.
  • Mass AI adoption is expected to exacerbate certain market-wide risks and biases, and at least one in five firms do not believe they are well placed to mitigate those. Firms are particularly wary of the potential for AI to entrench biases in decision-making, or to expose them, through shared resources, to mass data and privacy breaches. Nevertheless, many firms are involving risk and compliance teams in AI implementation, and those who do tend to be more confident in their risk mitigation capability as a result.
  • Long-established, simple machine learning algorithms are more widely used than complex solutions. Nonetheless, a large share of respondents is planning to implement natural language processing (NLP) and computer vision, which commonly involve deep learning, within two years.
  • Nearly half of all participants regard "Big Tech" leveraging AI capabilities to enter financial services as a major competitive threat.

2018.11.23 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."