In May this year, Gartner came out with a new report highlighting three key trends they have identified that will shape the Banking and Investment services sector for the duration of 2022. These include:

  • Generative AI
  • Autonomic Computing
  • Privacy-enhancing computation

According to Gartner, these three areas are already starting to gather momentum in this sector and will continue to grow over the next 2-3 years, with financial firms estimated to spend $623 Billion on technology products and services in 2022 alone!

But a worrying factor is that, although there is a clear need and demand for these technology services, many of the bigger, traditional firms are only starting to realise the opportunity they present. This is, in no small part, being driven by the emergence of smaller, nimbler neobanks and fintechs who have developed their own core services and are starting to gain a noticeable market share.

Generative AI

Generative AI is a broad label that’s used to describe any type of artificial intelligence that uses unsupervised learning algorithms to create new digital images, video, audio, text or code.

The application of Generative AI in Banking and Financial Services – or more specifically generative adversarial networks (“GANs”) – is found in fraud detection, trading predictions, synthetic data generation and risk factor modelling. Traditionally, many of these functions use IT but are overseen and run by humans.

Much of the reasoning for this is to do with managing internal risk and not trusting AI to make key decisions around business-critical factors such as trading predictions and risk factor modelling. This is a culture that needs to change.

With AI becoming increasingly “intelligent” and the ability of machine learning to adjust to its environment and needs, firms need to start looking at their internal processes to be more welcoming of these influences.

Gartner says this is now going to happen, but have the new modern, native digital banking and financial firms already stolen a march on traditional players who have yet to implement Generative AI into their processes?

Autonomic Computing

Autonomic computing (related to quantum computing) is a computer’s ability to manage itself automatically through adaptive technologies that enhance capabilities and cut down on the time required by computer professionals to resolve system difficulties and other maintenance such as software updates.

The move toward autonomic computing is driven by a desire for cost reduction and the need to lift the obstacles presented by computer system complexities allowing for more advanced computing technology to be implemented.

However, the underlying issue driving the growth of this trend is the fact that traditional banking and investment firms have an increasingly costly technology stack to manage – not to mention its complexity as it has been added to over the years.

Yes, Autonomic Computing is going to play a critical role in helping to manage this stack – but the truth is that the overall investment in managing it is only going to continue to spiral.

There is an argument for many financial institutions to start again when it comes to their technology infrastructure, but unfortunately this would be too disruptive to the services they offer their clients.

Modern day players such as digital investment platforms and neobanks have seen this and started to create niche solutions to eat into the big players’ market share. However, they will also need significant investment to reach their desired growth rates. Either way, clients are looking for their service providers to reduce their costs and increase performance, and this can only be done through the smart implementation of technology such as Automated Computing.

Privacy-Enhancing Computation

Though there is no standard definition there yet, privacy-enhancing computation aims to leverage a group of various technologies to enable the highest level of private data protection. This group of technologies supports privacy and data protection, and provides safeguards against violations and hacker attacks.

This element has become increasingly critical due to evolving privacy and data protection laws, increased scrutiny of AML, as well as growing consumer concerns. It uses a variety of privacy-protection techniques to allow value to be extracted from data while still meeting compliance requirements.

There are a number of companies out there now offering services to help address these needs. Some, such as ARIE Finance, have created a bespoke onboarding and e-KYC service based purely on the best in market privacy-enhancing computation along with the application of experience-generated business logic to deliver a faster and more resolute way of conducting KYC and AML.

Again, the technology is available for the traditional financial and banking firms to access, but it needs to be integrated with their existing legacy infrastructure – and this is where the cost lies!

In truth, all three of these trends will likely become part of the core technology needs of all financial services and banking businesses, and yes, the amount of money being invested presents a huge opportunity for those companies already offering these services. But we need to tread with caution as the $623 Billion needs to come from somewhere, and unless the company you bank or invest with is well on its way to implementing this technology, then it is the customer who will pay.