Yet the use of such emerging technologies also continues to grow exponentially across a host of industries, according to McKinsey. However, financial services organisations are likely to be the biggest beneficiaries with the adoption of such technologies in terms of actual bottom-line business value.

A critical reason for this is that these companies already have access to extremely complex and large datasets, which are a requirement to create different predictive models from this type of technology. Such models are also hugely powerful. They can be applied across a wide variety of financial products and situations, helping these organisations to better understand everything from the possibility and probability of defaults on loans, to customer purchasing intention to detecting fraudulent transactions.

Even the BoE and FCA believe that ML technologies can “make financial services and markets more efficient, accessible and tailored to consumer needs”.

Despite such advantages, to really harness the power of AI and ML and put in place for projects that make an impact with everyday consumers, organisations within the sector do still face some very specific and sizable challenges.

Firstly, financial services is a highly regulated industry whereby personally identifiable information (PII) is required to be protected. Yet this does hinder collaboration as a huge amount of time is necessary to clean and compliance check this information. Due to this, a project’s timescale can really lengthen.

Secondly, despite collecting and managing such a wealth of data, financial organisations face some limitations with the information they have. Even when data has been prepared to develop AI solutions, the actual dataset itself may be under-representative and such limitations are the most cited major barriers that prevent finance organisations from utilising their data assets. up to three quarters (73%) of data actually goes unused for analytics by companies, according to Forrester.

In fact, it is actually quite common that the most valuable information for an organisation is hidden in an under-representative customer category. A biased dataset means the insights gleaned will also be biased. The knock-on effect of this can be quite damaging. It can lead to false assumptions about customer segmentation that leads to higher costs for the acquisition of customers (banks already spend over £279 each year on acquisition per bank account), inappropriate offers being made to customers, which ultimately makes them less likely to purchase, or worse.

Its biggest impact could come in the area of personalisation of financial products to everyday consumers.

Advanced approaches using AI and ML are helping to tackle these challenges. Synthetic data generation technologies have emerged as a highly credible method of protecting PII, while also eliminating the limitations that organisations are facing with their data. The technology, underpinned by AI and ML, constructs a new, entirely synthetic dataset from the original information, one that is highly statistically accurate (up to 95%) but crucially does not reveal individuals’ PII.

It could be transformative for the financial services industry, with organisations like JP Morgan already touting its potential.

Its biggest impact could come in the area of personalisation of financial products to everyday consumers. Undoubtedly such personalisation has improved from tactics such as the ‘Fresno Drop’ which saw over 60,000 pre-approved credit cards mass-mailed to consumers in the Californian city in 1958. However, AI and ML technologies are built specifically to extract insights from data which encapsulates consumers' preferences, interaction, behaviour, lifestyle details and interests. Not only this, but the technology is also developing to such a stage that it can spot and, in effect, ‘rebalance’ biased datasets.

When this approach is implemented accurately, research has found that synthetic data can give the same results as real data. Yet crucially the key benefits include full data privacy compliance and a major reduction in the time needed for product development and testing.

While the successful personalisation of offers, policies and pricing makes a large contribution to the revenues of the business, it also keeps customers happy as they aren’t being bombarded by irrelevant information. This matters hugely as McKinsey found that highly satisfied customers are two and a half times more likely to open new accounts and products with their existing bank.

Having access to such deep insights into all segments is not something that can be put off much longer as consumer behaviour, across generations, is undergoing radical changes already. Research from PwC found that half of younger consumers (those under 35) will open a primary bank account based on a trusted referral from friends or family, by contrast, however, one out of two consumers over 35 will choose a primary bank based on the local presence of a branch or ATM. Such generational differences need to be spotted quickly to keep a financial organisation in step with rapidly changing preferences.

While it is encouraging that more and more financial organisations are using AI and ML technologies, any approach to maximise data’s value must have a coherent strategy behind it. Used in the right way and with the right strategy in place, the opportunity from these technologies offers unlimited potential to financial services organisations.