Understanding AI for personalisation

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Understanding AI for personalisation

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The term ‘artificial intelligence’ was coined in an August 1955 study proposal, by a group of academics and technology developers. The workshop on the subject took place one year later. In December 1955, Herbert Simon and Allen Newell developed ‘Logic Theorist’, which is today considered the first ever artificial intelligence (AI) program.

Since the middle of the 20th century, the horizon of AI has grown exponentially, in terms of its recognition, investment, development, and application. Never before have the merits and hazards of AI been so fiercely debated in public spaces.

Once sector taking a measured, steady approach to this nascent technology is financial services – the leaders of which see potential in numerous areas, not least experience personalisation. For many, AI is poised to re-define the concept of bespoke financial products – heralding the new age, of ‘hyper-personalisation’.

Personalisation of yesterday

It is important to remember that AI’s development – from the mid-1900s to present – has not been linear. Indeed, AI is an umbrella term, which encapsulates many different flavours of innovation. For example, predictive AI has been used for nearly 10 years, in underwriting and fraud.

Of course, predictive AI is unlike the headline-grabbing, fresh generative AI (GenAI) innovation, which we hear so much of today. GenAI uses large language models (LLMs) and natural language processing (NLP) to generate output in response to specific prompts. This could mean text, videos, or even computer code. That said, the various flavours of AI can be combined to improve how machine learning (ML) is applied, or digest data more rapidly.

At the delivery end of the pipeline, all this spells good news for the consumer – as well as firms. A Deloitte report revealed that Amazon and Netflix have respectively derived 35% and 60% of their sales from personalised recommendations.

Personalisation of today  

So, where is this heading and what does it mean for personalisation? Essentially, advancing AI has the power to make vital customer insights available to financial institutions at the optimal point of the funnel. According to William Perry, regional director, UK&I MEA, Medallia, such ‘real-time’ awareness can make or break customer experiences.

In a contributed Finextra article, Perry argues that personalisation goes “hand-in-hand with AI, so laying down the foundations for this technology is crucial.” He cites the importance of leveraging text, speech and video analytics – as well as AI-powered alerts – to achieve this aim.

Tracey Dunlap, managing director, customer experience, Jenius Bank, notes: “AI and personalisation technology advances in collecting and stitching together behavioral data across touchpoints, can enable banks to better understand each customer holistically. With these insights, digital banking can become more inclusive and effective for everyone, ultimately strengthening relationships between banks and consumers.”

“With natural language processing,” she continues, “AI chatbots can understand context and banking terminology when customers ask questions or request account support. Applications like ChatGPT signaled a shift in generative AI and have the potential to bring personalisation to an entirely new level.”

Dunlap goes on to underline that by utilising advanced AI algorithms, banks can analyse vast amounts of data and draw patterns – ultimately leading to tailored financial recommendations and problem-solving virtual assistants. Institutions such as Bank of America and Chase are already offering chatbots, though they are not yet as powerful as ChatGPT or other GenAI models.

Hyper-personalisation of tomorrow

With the dawn of GenAI, banking clients and customers can expect all manner of hyper-personalised services. Perhaps the most popular – or even the end goal – of these is the ‘super-app’.

An evolution of the personal finance app (PFA), and often referred to as the nirvana of financial personalisation, the super-app places the full gamut of banking services in the palm of customers’ hands.

Essentially, a super-app is a portal offered by an institution that has the capacity to fulfil consumers' sundry banking needs, via one user account. This might include automated cash flow analysis, transaction categorisation, smart budgeting, overdraft management savings tools, personalised advice, shared wallets, automatic bill payments, subscription management, investment options, savings accounts, credit scoring, lending, and more.

Today, American tech giants, such as Apple and Amazon, are leading development in super-apps, while Asia and the Middle East have already produced platforms such as Alipay, Careem, and Kakao. These wrap up in a single portal, mobile payments, credit scoring, ticket booking, ride-hailing, deliveries, voice calling, and media streaming. Of course, there’s also WeChat, which has added a digital wallet to its offering.

Clearly, the European financial industry and its regulators have their work cut out, with 90% of finance consumers welcoming the opportunity to organise their lives via hyper-personalised products and super-apps.

The year 2025

The year 2023 – with chat GPT just over a month old – introduced the public to AI’s sheer potential, while 2024 saw the roll-out of further practical applications and use cases in almost every sector, particularly streaming and retail.

The year 2025 could just be the year banks fully acknowledge that carpet-bombing their clientele with blanket offers of favourable credit card rates is less-than-optimal. Both evolving client expectations and vast leaps in data processing have meant that the deployment of AI and hyper-personalisation is no longer a ‘nice-to-have’ but a ‘must have’. Indeed, firms that excel at personalisation generate 40% more revenue; though the learning curve can cause many to bail before the rubber hits the road.

Perhaps, then, 2025 will see the financial industry continue to finesse its use of AI, broaden the use cases, and, crucially, explore how AI should assist – as opposed to replace – the work of humans. After all, what AI can never replace is humans’ capacity to forge empathy, relatability, and meaningful relationships.

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This content has been selected, created and edited by the Finextra editorial team based upon its relevance and interest to our community.