Data is fast becoming the engine that powers successful organisations, especially in the increasingly competitive financial services arena.
This year is shaping up to be the year that data sharing, alongside the use of Generative (Gen) AI, becomes a ‘must’ rather than a ‘should’. If businesses wish to remain competitive and cater to their customers’ evolving needs, then learning to capitalise on this opportunity is critical.
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Unveiling the new data landscape
The upcoming Data (Use and Access) Bill, introduced into Parliament in October 2024, will help. The Bill will have a significant impact on financial services firms and embedded finance providers by facilitating ‘Smart Data’ sharing, which in turn can fully unlock the capabilities that Gen AI promises. Those who are not prepared risk falling far behind.
It’s been estimated that the Bill will inject a £10 billion boost into the UK’s economy over the next 10 years by improving the way consumers, businesses, and asset owners can safely share data to help people make more informed decisions.
The Bill is set to give the Science and Technology Secretary and HM Treasury (HMT) wider powers to introduce new Smart Data schemes through regulations that will specify the scope of a scheme, including who is required to provide data, what data they are required to provide and when, and – importantly – how that data is then secured and protected.
It follows the introduction of its European counterpart, the European Data Act, set to take effect on 12 September this year. This aims to facilitate data sharing across the EU, establishing rules for accessing and using data – making data more accessible and usable while promoting data-driven innovation. It will be relevant for companies that operate across the EU as well as the UK.
These regulations are expected to create the right conditions to support the further growth of Open Banking, and new financial models that enable consumers and small businesses to access more tailored products and services.
Reaching the promised land of personalisation
Smarter data sharing will give consumers greater confidence and open up a plethora of new possibilities for ambitious businesses.
Firstly, training a Gen AI model with a much wider set of high-quality data will help expose the model to a more diverse and expansive range of information during its learning process, which then allows it to generate more varied, relevant, and representative outputs. In short, the better the data, the better equipped the model is to handle different situations and use cases, producing more nuanced results.
This will help providers to offer a more personalised service to customers. It’s fair to say personalisation in financial services has always been a topic that seemed just around the corner – yet never fully achieved. But thanks to advances in Gen AI, alongside a smarter data environment that it can now harvest, we’ve finally arrived at a point where we can efficiently analyse large amounts of data and genuinely improve the financing propositions and user experience we present to our customers.
In addition, using large language models (LLMs) will also enable embedded finance providers to accurately analyse a much larger volume of subsequent customer feedback, reviews, and calls between customers and customer service agents. LLMs can process this insightful data at a rate that was simply impossible previously, and the result is that providers can roll out a much more agile customer service that meets the raised demands of today’s digitally savvy consumers and small business owners.
The need for closer industry cooperation
In a smarter data ecosystem, closer industry collaboration and strategic partnerships will come to define success.
Logically, incumbent financial institutions should be much better positioned to leverage the latest advances in Gen AI because they have access to much larger pools of data and more data points. They should, therefore, be well positioned to build better algorithms.
However, history tells us different. The traditional financial services firms haven’t been particularly quick when it comes to executing innovative new initiatives. As a result, it’s likely that fintechs will continue to extend their lead in the customer experience arena, simply because they will build better algorithms, albeit with the fewer data points that they have access to. So one way in which traditional financial institutions can remain competitive is to partner with faster moving fintechs to find new ways to tap into their large pools of data to benefit their customers.
Gen AI’s interplay with data will have both a direct and indirect impact on customer experience in the financial services space. The direct impact is that LLMs will be used to better understand data and create the more personalised experiences that customers are looking for.
The indirect impact is perhaps more interesting. The world’s big tech companies – whether its Amazon, Facebook, or Uber – are typically more advanced in their Gen AI journey, and already using cutting-edge features to delight their users. As a result, customer expectations are rising rapidly and they will come to expect the same amazing experiences from their financial services providers.
It’s therefore advisable for financial services firms to carefully consider cross-industry learnings before they step further into Gen AI. They should work out what they can learn, and apply, in order to quickly appease their customers. And of course, with the nature of embedded finance, they can also work directly with the big tech platforms to integrate their services and meet consumers where they are present.
A new age of data requires new thinking.
Nima Montazeri is Chief Product Officer at Liberis
