For years, financial media has been awash in warnings of an impending tech-pocalypse of job loss and industry consolidation. Those dire prophecies have been slow to materialize — as is often the case when a reimagined future bumps up against the unvarnished reality of tech’s diffusion into bustling businesses bound to legacy infrastructure.
Nevertheless, tech is reshaping the financial workplace — and those in it. Today we’ll explore a few of the ways that’s happening, as reflected in recent media coverage.
A September McKinsey report found that AI could add 16%, or $13T, to global GDP by 2030 — good for 1.2% productivity growth annually. The estimates dovetail with a PwC report forecasting 14% global GDP growth by 2030 owing to AI advances.
The reports further found that “AI front-runners will gain an enormous competitive advantage” by collecting consumer data to refine AI models and boost their products’ appeal.
Advantages at the national, firm, and individual levels are likely to assert themselves gradually, however, due to learning curves and the need for complementary co-inventions, business models, and regulatory policies.
How, then, are AI and technological change more generally asserting themselves at this stage?
In some cases, they are unfolding as ominously as doomsday preppers predicted. BAML CEO Brian Moynihan recently said his firm has slashed its workforce from a peak of 305,000 workers to 204,000 currently.
But the diffusion of technology is also catalyzing subtler shifts within financial firms such as the growing ubiquity of coding skills. Banks like tech trailblazers JPMorgan (+13%) and Goldman Sachs (+14%) upped their tech spend this year, with much of swelling tech budgets earmarked for hiring programmers.
Given that dynamic, bankers and traders are increasingly feeling the need to learn coding skills to stay employed. Former Goldman Sachs head of Asian equity sales Junta Nakai embraced that challenge by taking online courses in data science, taking tech colleagues out for coffee, and eventually joining an AI-based fintech startup.
For traders looking to break into machine learning concepts, an October eFinancialCareers article touted Andrew Ng’s machine learning courses on Coursera and Stanford’s website as well as Princeton’s Coursera course on bitcoin and crypto.
For those seeking material more specific to finance, JPMorgan recently followed up last year’s “excellent” (but massive) guide to big data and AI in finance with a guide to machine learning in algorithmic trading.
JP Morgan meanwhile opted to make coding lessons in Python mandatory for its incoming analysts in investment banking and asset management this year.
The growing universality of coding has generated pushback at times. One (anonymous) eFinancialCareers opinion piece called “these new ‘trader-coders’...a problem for the real coders in banks.”
Another embarked on a soaring dialectical journey questioning whether Python was really the programming language best-suited to data science in finance. (The answer is ‘yes.’)
The proliferation of programming is, in turn, encouraging firms to explore ways to streamline the efficiency of coding. Credit Suisse and JPMorgan have gone meta, automating aspects of the development process and closely tracking teams’ productivity.
The pervasiveness of coding is even reshaping the office in physical and social terms. Morgan Stanley is redesigning 1.2M square feet of office space, knocking down walls to “put technology experts closer to brokers, traders and bankers.” Dress codes have also become more casual — a nod to tech’s informal ethos — and group meditations are on offer.
Those initiatives reflect a growing conviction that the way forward lies in close collaboration between firms’ technology and business experts to minimize tension between the two, reap the benefits of cross-pollination, and break down organizational resistance to change.
In that environment, the ability to learn coding skills is proving indispensable. But arguably the most important (and most challenging) skill for financiers to learn is, ironically, that of letting go of their existing skill sets and embracing the need to learn anew.