Like most sectors, Financial Services is anticipating (and already using) automation and AI tools to reduce manual work, improve productivity, and reduce costs. 75% of firms are already using AI, which is a dramatic increase on two years ago, when the figure was 58%.
The impact of AI’s advances is hard to overstate, but its transformation will not simply make the normal run of business more efficient — parts of the sector will look fundamentally differently, and in some areas the skills and the approach to Financial Services may shift to fit an AI-powered world.
AI promises efficiency, cost savings, and a better customer experience in Financial Services
First, a top-level quick summary of the benefits and impacts of AI in Financial Services.
Automation
Just over half of financial AI uses feature automated decision-making. 24% of those programmes are semi-autonomous, requiring human oversight for certain questions but otherwise making decisions independently.
For functions that are safe to delegate to AI, freeing up humans for higher value or more sensitive and ambiguous matters clearly represents a commercial opportunity in cost saving a growth potential.
Richer data with speedier, deeper analysis
AI’s ability to interpret data sets faster than a human can offers extra time to use the data and strategise with it.
AI-powered programmes also offer a more democratised relationship with data — Natural Language Processing (NLP) can empower any user to generate reports using conversational programmes, rather than requiring coding skills. That means more and better data available to more users.
Customer experience
While the modern customer experience in, say, retail banking is increasingly impersonal, without visiting branches or interacting with people, AI can make it more personalised. Drawing on rich data sets, AI will be able to tailor experiences, plus make tools like chatbots more adaptable, intelligent, and effective.
The implications of AI for skills and approaches in Financial Services
There is much more to say on the impact and potential of AI on business functions in Financial Services, but what they mean on an operational level is much further reaching than a more efficient business-as-usual.
Emerging (and declining) roles as a result of AI
When tedious, basic, or repetitive tasks and processes sit with AI instead of people, the shape of departments and people’s roles within them will inevitably shift.
For example, most organisations will need AI specialists and data scientists (but will probably find it more cost effective outsource that need to a partner). There will also be a role for ethics officers to safeguard policies and monitor practices.
Analyst roles will likely shift towards strategy and decision-making, with some of the basic ‘data crunching’ handled by AI, and there is less human involvement in interpreting and presenting data.
How wider financial markets could change in an AI world, and how to respond
On the topic of democratisation, AI tools are not only available to banks and financial institutions. An increasing variety of AI tools will mean that the barriers to entry for investment are much lower than they have traditionally been. Coupled with app-based, accessible brokerages and investment platforms AI tools will mean that members of the wider public can more easily become players in the financial markets.
Unlike markets, peers, and rivals whose behaviour is roughly predictable, individual investors can shift markets in unexpected ways.
As an extreme but instructive example, look at the 2021 GameStop short squeeze.
In late 2020, the Reddit community r/wallstreetbets was in feverish excitement about the potential undervaluation of shares in the bricks-and-mortar video game retailer GameStop. Prominent users also noted that certain hedge funds had taken short positions on the stock, and that buying the stock would raise its price and leave the hedge funds unable to cover their short positions.
Crucially, the US government had provided citizens with Economic Impact Payments to assist with living costs during the Covid-19 lockdowns, which meant that many people suddenly had surplus lump sums that they could use to buy stocks. Driven largely (but not only) by individual private investors and Reddit users, GameStop’s share price rose dramatically, causing short sellers to buy the stock to limit their losses on their short positions, and increasing the stock price even further. The event caused huge losses for certain funds. Melvin Capital, for example, lost 30% of its value.
So, what does that mean for Financial Services, AI, and roles within it?
Ultimately, it might mean even (or especially) in a world of AI and automation, the most vital skill will be understanding human behaviour.
The certainty of institutions can now erode suddenly — a stockmarket is not only influenced by organisations whose drivers are familiar and dependable. Unconventional investors who are motivated by, for example, spite and boredom, can undermine the certainty of ‘heritage’ Financial Servcies.
In a world where quantitative research is democratised, and investment platforms are available to more or less anyone, unconventional psychological and behavioural expertise will be essential. Radical, and out-of-the-box thinkers will be required to balance the heritage thinking of financial institutions.
How Financial Services can prepare for AI
Safe, successful AI starts with understanding the AI maturity of your organisation. To ensure that you do not violate your own AI policies or those of legislators, you must appraise your experience and literacy with AI, how you currently engage with it, and the level of AI sophistication and understanding of your team:
- knowledge of the types of AI and automation that exist
- understanding where automation, LLMs, and NLP are already functioning
- appreciation of the risks associated with the tools (bias, security, intellectual property etc.)
- best practice when engaging with or using AI
Having laid that groundwork, many financial organisations discover that ‘off-the-shelf’ AI tools are inadequate for their goals. In that case, a bespoke model will be required.
In both cases, external partners offer the most cost-effective and dependable expertise.
As Data Advisory experts, and having developed an AI artefact assessment model based on UK and EU regulations, Agile Solutions:
- can manage your AI strategy and implementation, providing the full data management end-to-end.
- offer active account and project management working for you to deliver your goals, not in rigid projects that deliver ROI only after many months, but in agile sprints that show returns incrementally
- are certified and experienced consultants working within the AI EU Governance and UK frameworks
To speak to a data, AI, and strategy expert who can assess your relationship with AI, discuss your goals, and make strategic recommendations for your AI initiatives, get in touch.