Working With The Robots – Man and Machine Together

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By Joanne Graham in Machine Learning on Mar 27, 2018

Baroness Lane-Fox of Soho, better known as Martha Lane-Fox, the online entrepreneur who made both her name and her fortune with is certainly a name to be trusted when it comes to technology. But while she’s a strong advocate of the digital age, her recent speech in the House of Lords last September sounded a cautionary note.

‘Extraordinary leaps in…machine learning somehow feel dislocated from the people who will inevitably be affected by the ways these innovations are deployed.’[1]

Her point is a salient one. While machine learning has lots to offer to the financial market – enhanced data handling capacity, sharper risk analysis and ultimately, bigger bottom lines – it also has lots to take, and no starker battlefield exists between man and machine than employment. Data from salary benchmarking site, Emolument, showed that last year, 47% of employees within the financial sector feared losing their job to a machine, while an Oxford University study recently suggested that up to 35% of existing UK jobs could be at risk of automation within the next 20 years, a truly frightening statistic for those of us committed to climbing the corporate ladder. In real terms, traditional roles in asset management, private banking and risk analysis are on the decline, as technology moves in to automate these processes.

But where there’s bust, there’s boom, and the financial industries are known for their resilience. So while machine learning reduces both the manual and mental energies needed from people, new and exciting fields are opening up, which offer very real revolutions for expansion and opportunity. David Brear, digital banking entrepreneur, describes this crossroads best –

‘Technological innovations will be the heart and blood of the banking industry for many years to come, and if big banks do not make the most of it…large technology companies surely will.’

So, while the instinct may be to fear it, don’t – embrace it.

For a start, let ‘fintech’ – or ’financial technology,’ – work for you. Describing those new, agile start-ups that support the digitisation of finance, from crowd-funding to mobile payments, fintech is on the frontline of all of which helps to streamline and simplify banking and business for the screen generation. Not just a fad, fintech is a force to be reckoned with, with investment in this nascent industry rising twelvefold in just seven years, to $12bn by 2015.[2]  Fintech also offers direct investment research, which takes the work out of calculating where best to place those client funds, and for those companies bold enough, buying your own slice of the fintech pie can reap rich rewards, such as Fidelity Investment’s purchase of eMoneyAdvisor in 2015.

New technology invariably leads to new careers, and a fusion of technology and finance means that quantitative scientists, or ‘quants,’ who specialise in mathematical and statistical modelling, are on the rise, while data scientists are needed to marry the information with meaningful and accessible analysis. Historically, new technology has assisted businesses to cut costs while boosting productivity, so savings made through machine learning will afford expansion elsewhere.

Finally, and perhaps most importantly, machine learning is not set to destroy jobs, but alter them, freeing up our time to move away from the mundanity of routine and rigor and instead concentrate on the more creative and conceptual sides of work, from brainstorming and lateral thinking to vital soft skills which will forever remain beyond the remit of a machine. For while machine learning is well-equipped to take control of what can prove fallible to man, it cannot replicate the unmistakable human touch. People, rather than machines, will always remain the critical currency in building and developing business relationships, so take heart, not cover.




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