Today’s discourse on the nature, consequences and regulation of artificial intelligence (AI) can often obfuscate the technology’s immediate, on-the-ground applications. In this article, Finextra considers 5 distinct ways in which AI may be used by financial
institutions to better serve their customers.
Five use cases
The proverbial secret sauce in
AI is its knack for sorting through enormous amounts of data, spotting patterns, and using the learnings to make predictions about customers’ needs. But it isn’t all product-flogging and bloating profit margins; banks will see AI give relief to their service,
advice, and information commitments.
Artificial intelligence’s pièce de resistance, then, is marking the important moments in a
customer’s journey with predicitive, relevant, useful and personalised support – until her ambition is met.
Here are five use cases for AI in personalisation.
1. Overdraft management
According to the Guardian, “UK banks have
2 million customers stuck in permanent overdraft.” Financial challenges for the average working person have only sharpened with the advent of the cost-of-living crisis.
The deployment of AI to help customers avoid overdraft charges is, in this light, a move hard to overvalue. By tracking the points at which a customer typically enters her overdraft (for example, when a recurring, monthly direct debit is pulled by a utility
company), AI mechanisms can flag this to the bank and have an automated, personal, notification forwarded to her. Upon receipt, the customer has the option to move cash across and, thus, avoid overdraft fees.
Options within this use case may extend to the supply of a bespoke budgeting tool to help customers rationalise their cash flow between paychecks.
2. Saving goals
Alongside budgeting support, banks may wish to use AI for personalised saving structures.
If, for instance, a customer voices that she is saving for a house deposit, AI technology can both identify negative spending patterns that prevent the accumulation of disposable cash, and flag retail options or deals to ensure less is spent on a month-by-month
basis. This may amount to a personalised saving plan – based on the financial goal and how quickly it must be reached.
To boost customers’ participation in the saving process, AI-enhanced gamification – the application of typical elements of game-playing, such as point scoring or competition, to other areas – is a technique being increasingly reached for.
Monzo, for instance, “uses gamification to provide insights into spending habits and offers various challenges and goals to help users manage their finances.”
3. Personalised communication
Perhaps the most recognised application area of AI in the drive to personalise financial services is customer communication. Whether it be chatbots, virtual assistants, or an AI
concierge-of-sorts, the outcome is the same: the opportunity for customers to gain a direct line with their bank, around-the-clock, with access to support that is accurate and bespoke.
There are perks for the institution too; customer queries are thereby triaged and the burden on physical call centers is reduced. In the case of Lloyds TSB, AI-enabled in-app messaging with customers is reported to have increased lead generation by
300%.
A popular example of personalised AI-communication is the Bank of America’s virtual assistant,
Erica, which is accessed via their mobile banking app and helps end-users get the most out of their money.
4. Credit scoring and lending
Gone are the days of stuffy credit scoring models hinging on finite data sets, like income level. Artificial intelligence is heralding an era of fresh, shiny and equitable lending services – incorporating considerations like “social media interactions,
internet usage, and past transactions, in order to determine if someone is creditworthy,” says the
European Financial Review. “This leads to improved credit rating, allowing financial institutions to offer credit to a wider range of candidates.”
5. Tailored investment guidance
Artificial intelligence also has the potential to provide personalised investment recommendations, which stands in contrast to the alternative, antiquated approach of bunching clients into groups delineated by risk appetite.
By drilling down into an individual’s behaviors, interests, budget, and motivations, AI has the power to suggest highly relevant investment options to clients. What is more, the technology will, characteristically, refine subsequent recommendations in line
with how the originals were received. Perhaps the investor in question is interested in supporting ESG projects; the tool will pick up on this and provide access to that market.
Tailored investment guidance is a constantly evolving interplay between AI and the client, standing to strengthen the institution’s relationship with its base, as well as more readily mobilise private capital.
Personalisation will be crucial for 2024 and beyond
In an
article for Finextra on the topic, Alex Jimenez, managing principal, financial service consulting, EPAM, says that given the capabilities of AI, personalised customer experience is the future: “The industry is not far from being able to offer this level
of personalisation, even when the bank does not have a lot of data on someone. Taking a leaf out of the likes of Amazon, Apple, and Google’s books, which are great at personalising experiences, banks must learn how to brand their services and do a better job
of presenting themselves as being truly customer centric.”