– Helene Panzarino
Anthony Jenkins, former Barclays group chief executive, has said that 50% of all banking jobs are set to be replaced by artificial intelligence (AI). Deutsche Bank’s former chief executive, John Cryan, has given similarly stark predictions. Andy Haldane, the Bank of England’s chief economist has warned of the ‘dark side’ of technological revolutions, indicating that AI could lead to disruption in the job market on a similar or greater scale than the Victorian era industrial revolution.
50% of all banking jobs are set to be replaced by artificial intelligence (AI)
Faced with these predictions, professionals in all branches of finance are understandably trying to identify the implications of AI and machine learning for their organisations and careers.
From my work with banks, SMEs and start-ups working on fintech projects, I believe that focussing on three areas will help professionals working in finance to succeed in the age of AI and machine learning:
- Understanding the technology and its strengths and limitations
Marketeers are making the most of the hype around AI and machine learning with ‘powered by AI’ becoming ubiquitous in marketing literature for a wide array of tech products. It can be easy to fall into the trap of thinking AI can be readily applied to almost any business problem if you don’t have a fundamental grasp of the technologies.
Sound technological understanding across an organisation, not just in the IT or innovation departments, enables better investment decisions when choosing technology partners and evaluating organisational readiness for technological change. UBS research has shown that many financial institutions and marketplaces are not ready for the potential disruption of AI. Barriers include decades-old legacy IT systems. It is easy for executives to rush into projects for which their organisation isn’t ready.
It is easy for executives to rush into projects for which their organisation isn’t ready
Technological understanding is also vital for security and risk management. Employee skills must keep pace with technological change to supervise AI and machine learning models, ensure regulatory compliance and to avoid unintended issues. Pursuing AI and machine learning projects without sufficient understanding of the technology, in particular its reliance on the strength of underlying data and how the algorithms have been ‘trained’, can lead to expensive and embarrassing mistakes. We saw an example of this when Amazon stopped using machine learning in recruitment as its system was trained on a data set with a gender bias.
Executives who will make the best decisions in the AI and machine learning era will be those who take time to understand the technology and its strengths and limitations.
2. Retaining customer centricity
Successful use-cases of AI are orientated around a clearly defined business goal which benefits both customers and the wider business.
For example, the use of chatbots by banks to improve customer experience and provide immediate round-the-clock assistance is becoming prevalent. In financial security HSBC has made significant and successful investments in AI and machine learning to combat fraud.
Accenture estimate that AI will add $1.2 trillion in value to the financial industry by 2035
Accenture estimate that AI will add $1.2 trillion in value to the financial industry by 2035. Added value will come by companies using AI to improve the products and services they offer, as well to save money by automating back office processes. Executives who will lead in the AI and machine learning era will be those focussing, not just on cost savings, but also on growth opportunities which use technology to amplify the employee and customer experience.
3. Collaborate to innovate
Finally, we are seeing a rise in collaborative working in particular between start-ups, scale-ups and large incumbent financial institutions. More than half of the respondents to a survey of CEOs by KPMG saw third-party partnerships as the route to digital disruption, with start-ups very much at the centre. Instead of dismissing the fintech players as a fad or just a few upstarts taking crumbs from their table, traditional banks have now started to realise that if they were to stay relevant and competitive they should be partnering to embrace new technologies. Likewise, fintech SMEs hitherto thwarted in their efforts by a tough regulatory environment and other barriers to entry, not least the pervasive inertia of both retail and corporate customers to trust little-known financial brands, are realising that partnership with incumbents is the way forward.
Professionals who will thrive in the era of AI and machine learning will be those who keep their knowledge of these quickly evolving technologies current, seek out opportunities to add customer and client value, and who are ready to embrace new perspectives by collaborating within and outside of their organisations.
Helene Panzarino is Managing Director of Rainmaking Colab, a fintech consultancy. She is also Director of Digital Finance portfolio at Imperial College Business School and teaches on the Fintech – Innovative Banking and From Data to Decisions – Machine Learning & AI in Finance executive education open programmes.