Published by linker 5
Posted on October 6, 2020
6 min readLast updated: January 21, 2026
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By Gordon Graylish, Programme Director of the AI & Machine Learning in Financial Services virtual programme at Imperial College Business School
For a number of years now, there has been much discussion of the 4th industrial revolution. Industrial revolutions are always greeted with fear. Steam, electricity and computing, the three previous revolutions were greeted by fear too – however, at the same time they resulted in massive innovation, much like the 4th Industrial revolution today. In the 1800’s, Primary jobs based around farming, fishing and mining accounted for over 80% of roles, now they are just 1.3% of the UK workforce. On the other hand, Tertiary jobs including transport retail and services moved from less than 15% to 84%, which as a result of, we see a massive increase in societal wealth.
Today, we are in a fourth revolution, referred to by the World Economic Forum as “cyber-physical systems” and driven by the growth of intelligence in our digital systems. In the Finance sector, we see the lead vehicle of this as Artificial Intelligence.
Utilising AI can give you the edge
The Financial services industry lives by trust. Today, digital technologies such as AI, distributed ledger, and ubiquitous computers are massively increasing competition. CEOs can no longer say “but I don’t understand these technologies”, or “I do not need to understand these technologies” if they are to successfully guide their companies in the future. These technologies have two imperatives, reduce costs and initiate new offerings. Leaders who don’t understand them will simply be left behind.
AI is impacting all aspects of finance, from digital onboarding, compliance and fraud detection, to loans, investments and to a radical increase in efficiency.
To do this, AI is completely changing the information that we use. In the past, 80% of information was internal to the organization, within five years, that will reverse with 80% being external – it just simply cannot be ignored by finance professionals anymore. All major financial firms have multiple efforts underway with AI and it is of course a huge focus of the startup and disruptor community.
This growth has fundamentally changed the focus of the computing and cloud services industry. Semiconductor firms, such as Intel and Nvidia, list AI as their prime focus, as have Microsoft, Amazon web services and IBM. The focus has shifted from CPUs to GPUs to neuromorphic computing with an astounding increase in capability. This has had a disruptive impact on cost and capability.
The statistics simply prove this further. From 2017, the costs of training a common image algorithm has dropped from $1000 to $10, the costs to classify a billion images is even more radical, a drop from $10000 to .03 – it’s clear they have a disruptive impact on costs.
And recent events surrounding covid-19 have also shown us just how important this AI is when you step outside of the finance space too. The first discovery of the fact of the epidemic was from Blue Dot, an AI firm that uncovered evidence of an outbreak 2 weeks before traditional sources. AI then enabled the modelling of the actual virus in weeks and the assessment of 1000s of existing medicines for effectiveness against the virus. And now, we are looking at 10s of potential vaccines and treatments within months rather than years, all thanks to AI. It’s clear to see that the impact of AI, not just in finance, can be an extremely positive one.
But AI is not all plain sailing
However, all of this progress with regards to AI does not come without risk. In many cases the AI application is a black box. This can result in bias that the developer did not intend. Some early examples of this include unforeseen bias in lending to women from Apple’s credit card algorithm and also in mortgage lending to minorities, based on previous data points. This is, of course, unacceptable but is a challenge and risk nonetheless from using black box algorithms. Another raw example in a different sphere was the A level debacle with historical bias against some schools. As a result today, there is a huge focus on explainable AI. Just in the past weeks, Rolls Royce has released an AI ethics framework to try to automate and measure this. Other tools based approaches are being made to assist in testing, and are needed to challenge these risks.
Like all revolutions, it is at times messy, and will progress in fits and starts. In this case, the pace of change and the massive changes in storage and communications magnifies this. There is no potential that artificial intelligence will not massively impact insurance, banking and investment.
The benefits of AI are not a given, they will only be positive if those who are implementing it are knowledgeable and have the expertise. Simply not engaging in investment in AI is no longer possible for CEOs and finance executives, they’ll be left behind and beaten by the competition if they don’t invest. However, blind investment and not understanding how to utilize AI is just as bad, the risk to brand and in the end, business, is enormous.
Education is the key to success
Now more than ever, Leaders and executives have to learn the inside out of AI in their industry, or incur competitive impacts and unacceptable risk. Programmes like the Imperial College Business School online AI & Machine Learning in Financial Services are there to educate leaders about these AI applications in their industry and are vitally important for leaders in understanding the realm of the possible and also its risks
I would urge Leaders to update their knowledge and understand AI – it’s vitally important for the future of their companies that they do so.
Bio
Gordon Graylish is the Programme Director of the AI & Machine Learning in Financial Services virtual programme at Imperial College Business School. Gord has held a number of senior roles at Intel including Vice President and General Manager of the Governments and World Ahead division. Gord was also a commissioner of The UN’s broadband commission and was previously Vice President and General Manager of Enterprise Solutions for Intel.
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