AI Opportunities Action Plan: what can we learn from financial services?

The UK currently lacks a concrete AI infrastructure. However, within the context of the new AI Opportunities Action Plan, job creation is guaranteed through the building of data centres and increasing the UK’s computing power. In establishing these opportunities, some 13,250 jobs are expected to be created.

Banks spend nearly £219.7 billion each year on tackling financial crime. It’s a difficult – and often thankless – task, given the sheer number of transactions that must be monitored to weed out the fraudulent ones. International collaboration might be required and red tape raises further barriers to identifying and preventing these transactions in time.

AI’s ability to digest and analyse huge datasets means it can change the outlook for anti-fraud teams, who can now monitor more information than ever before. For example, high street bank TSB has been utilising machine learning to provide every individual transaction with a score based on how likely is to be fraudulent within milliseconds.

The bank estimates a 20% reduction in push payment fraud – where users are convinced to send money to people pretending to be someone else – as a result of the technology.

A human touch is needed here, too – predictive AI might be able to identify spending patterns and catch fraudulent transactions before they happen, but a human understanding of why a transaction might have taken place in a certain way is necessary to interrogate individual payments on a case-by-case basis.

AI advancements in recent years have enabled huge improvements in financial forecasting. Given the increasingly volatile nature of the competitive landscape, real-time updates to your forecasting can be the difference between getting ahead of the game and being left behind.

Machine-learnt algorithms can provide automated forecasting that continuously adapts projections, aggregating massive datasets from a range of sources and in a range of mediums. These can be compared to industry benchmarks or competitor performance to ensure that your firm is on track, according to your KPIs.

And, of course, as time passes and a growing amount of data is entered, these AI predictions will become increasingly accurate. When used in this context, AI may be able to identify the real driving factors behind a business’ revenue. In one case, a global business found that its units sold and sale price, traditional indicators of high revenue, had far less impact on its overall profit and loss than expected.

Stuart Wilkie, Head of Commercial Finance at Anglo Scottish, comments: “As machine learning becomes more and more accurate, there’s essentially no limit to the predictions artificial intelligence may be able to make.

“Given that high-quality predictive AIs are a reasonably new phenomenon, we can expect forecasting to become more accurate, span longer periods and account for a wider range of events, as we continue to feed large-scale datasets through it.”

Given modern AI’s surgical approach to forecasting and its ability to pull from a wide range of different data sources, it’s unsurprising that AI is being used to predict the best-performing stocks to invest in.

In fact, a recent study found that 71% of UK investors would trust AI to recommend products for their portfolio – an 8% rise from 2022. In the US, 45% of investors using tips website The Motley Fool said they would be comfortable investing based solely on ChatGPT’s advice.

Investment advisors are benefitting from the ability of machine learning tools to quickly analyse a portfolio and identify areas of risk. In line with identified risk areas, they can design a newly diversified portfolio based on each customer’s strategic goals, choosing the perfect blend of cash and ETF investments.

AI’s ability to handle menial, repetitive queries with greater efficiency than its human counterparts has led to the improvement of customer support chatbots. And – thanks to advancements in natural language processing (NLP), the branch of AI concerned with giving computers the ability to understand text and speech in the same way that we can – chatbots are providing a better service than ever before.

Let’s face it, we’d all rather have a human operative deal with our queries – but conversational AI is now able to handle simple, one-size-fits-all queries with ease. In the event of a more complex issue, they’ll send you through to a human customer support employee.

With 79% of financial services leaders aware that a personalised experience increases customer retention, the use of chatbots for standardised tasks frees up manpower to personally deal with more important issues. The bank benefits from increased efficiency, while the end users benefit from more readily available customer service for complex enquiries. This approach could help businesses in a range of sectors to win going forward.

Given the speed at which technological advances regarding AI are taking place, it’s important that both the government – and the businesses using it – understand its potential implications. Starmer’s growth-first approach to AI has been at odds with the previous government, which was decidedly more cautious and safety-focused.

At present, the onus lies with the that are businesses using AI to manage the way in which they implement it. Long-term strategies are vital in managing AI usage at the corporate level, but as of early 2023, 57% of businesses are currently taking a reactive approach to artificial intelligence.

With this in mind, and with AI growth a key goal for the new government, some are questioning whether a centralised regulatory structure for AI will be required. With a growing number of businesses adopting AI in new ways, the government faces something of a balancing act in terms of promoting technological and economic growth, while safeguarding businesses and users.

Until then, any firm using artificial intelligence should do so strategically, advises Wilkie. “In the meantime, individual businesses must take a considered approach to their AI activities. Understanding how AI fits into your firm’s long-term strategy enables a deeper interrogation of your AI usage, ultimately leading to the safer and more sustainable use of artificial intelligence.

“By creating a detailed AI strategy, you can also futureproof your business against any legislative changes which will take place in the coming years.”

Stuart Wilkie is Head of Commercial Finance at Anglo Scottish

“AI Opportunities Action Plan: what can we learn from financial services?” was originally created and published by Leasing Life, a GlobalData owned brand.

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