How is AI reconfiguring financial services and how sophisticated is machine-based learning expected to become? Considering some of the important trends is Stuart Wilkie, Head of Commercial Finance at Anglo Scottish.
The executive looks at key strategic areas like precision forecasting and improving customer services as part of the continuing digital transformation of financial services.
Precision forecasting
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 a firm is on track according to any of its key performance indicators (KPIs).
As time passes and a growing amount of data is entered, the AI’s predictions will become increasingly accurate. When used in this context, it may be able to identify the real driving factors behind a business’ revenue. In one case, a global business found that units sold and sale price, traditional indicators of high revenue, had far less impact on its overall profit and loss than expected.
Wilkie tells Digital Journal: “As machine learning becomes more and more accurate, there’s essentially no limit to the predictions artificial intelligence may be able to make.
Wilkie adds: “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.”
Investment insight
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.
A recent study found that 71 percent of UK investors would trust AI to recommend products for their portfolio – an 8 percent rise from 2022. In the US, 45 percent of investors using tips website The Motley Fool said they would be comfortable investing based on ChatGPT’s advice and nothing else.
Investment advisors can benefit from machine learning tools’ ability 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.
Customer service
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 we can, chatbots are providing a better service than ever before.
With 79 percent 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, and the end users benefit from more readily available customer service for complex enquiries.
Managing, monitoring and improving AI use
Given the speed at which technological advances regarding AI are taking place, it’s important that businesses using AI understand its potential implications. The British government recently hosted the Bletchley Summit, during which 28 governments from around the world – including China, the EU and the US – agreed to work together on AI safety research. For now, however, there is little in the way of international legislation.
The onus therefore lies with the 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 percent of businesses are currently taking a reactive approach to artificial intelligence.
McKinsey, one of the leading adopters of AI at a corporate level, set out a 66-page document in 2021 with a roadmap to the “AI Bank of the Future.” The introduction extolls the importance of “formulating the organisation’s strategic goals for the AI-enabled digital age, and [evaluating] how AI technologies can support these goals.”
Wilkie comments: “AI adoption can have an almost instant impact upon a financial organisation’s operating practices, and by proxy, its bottom line. With that in mind, it can be tempting to rush through AI integration at various levels of the business. Wilkie closes out with the following summary: “However, a considered approach is utterly vital. Understanding how AI fits into your firm’s long-term strategy enables deeper interrogation of your AI usage and ultimately leads to 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.”
