What does AI mean for financial services?

Given the amount of hype and media coverage of Artificial Intelligence you would be forgiven for thinking it was a brand new technology. In reality it has been around in various forms for at least a generation.

But today’s AI is reaching a tipping point because of improvements in storage technology in terms of both speed and cost, in computing power and in the advanced analytics which can properly exploit both these technologies.

Matt Cox, Nationwide Building Society, said: “There is a new breed of emerging technologies that can take in and make decisions on huge amounts of data, especially unstructured data including images and speech, and there has been a massive step up in the capability to do that in the past few years. Now that we have all this data to apply algorithms to, this is driving a new wave of interest – and all these techniques are possible now at scale, in a way that they just were not before.”

What are the challenges to a successful AI project

The building block for any AI project is data. The old computing adage of ‘garbage in, garbage out’ remains true however big your data lake is. The crucial first step is ensuring the data being used is accessible, clean, useable and reliable. Getting data out of legacy systems and into a functional form for analysis is often the toughest part of any project and is all too easy to overlook.

For financial institutions with systems which have grown organically over time this can require some re-engineering of back-office functions, not simply adding an AI system to the front.

Getting the right skills in place, whether by recruitment or training, is of course vital. But most successful projects also rely on several partner arrangements with other companies. Billions of pounds and dollars have been invested in AI start-ups in recent years, taking advantage of this expenditure will accelerate returns and stop projects “re-inventing the wheel”.

But for financial institutions there is unlikely to a be a single provider of a plug and play solution. A successful project is more likely to require co-operation with several providers.

There also needs to be a recognition from everyone across the organisation that AI is more than just a technology project.

To truly succeed it needs a cultural change across the institution. AI needs to be used by all business units and staff within those units need to be open as to how the technology may impact on their working lives and on the services they deliver.

This process will also need to reassure people that AI can improve their work and is not designed just to replace them.

How is AI being used in financial services right now?

Early stage projects already showing success include customer services and fraud detection. Voice recognition, virtual assistants and chatbots are becoming part of customers’ lives elsewhere and are being adopted by forward thinking financial institutions too.

As people get ever more used to talking to a machine for at least the start of interacting with almost any institution so its use by banks will become more and more accepted.

AI is already playing a big role in detecting cyber-attacks and finding fraud and money laundering attempts. This will only increase because pattern detection is a relatively easy task for AI and extremely useful for spotting dodgy transactions.

What’s next for AI?

Making AI a key part of your financial institution means making sure the staff and customers are equally comfortable with the change. Financial services face a tougher regulatory burden when it comes to data protection and audit trails which will not reduced by intelligent systems. This is especially true because it seems that public attitudes about data privacy and protection are finally toughening up.

That means that AI, or any other form of data processing, must not damage the good reputation most financial institutions have for good data practises. With these provisos in place AI can begin to improve customer services by creating truly personalised offers and automating processing.

Once that is working financial institutions should begin to see faster and better AI-driven decision making and the creation of completely new products and services.

This piece was informed by the research done by a research paper by Finextra titled: “The Next Big Wave: how financial institutions can stay ahead of the AI revolution”.