AI's Role in Digital Transformation for Financial Services

Richard Allman, Impact Strategy Lead - Financial Services at SparkBeyond

In a world economy overshadowed by the fallout from Covid-19, banks and the FSI industry as a whole are looking for ways to remain relevant, solvent, and at the forefront of clients' minds. They must do this while not being seen as the ‘bad guys’ of the world economy.

So far they have managed well. As we look to the future, and the possible shocks yet to come, what role does AI have to play in this mission, and what broader digital transformation is needed to ensure these organizations stay stable and continue their growth?

Growth in an age of physical remoteness

It is clear that as consumers become used to living and working from home, many tech companies have seen their share price skyrocket from the effects of this behavioral change - with Amazon being a notable example.

The share price of banks, however, have broadly gone the opposite way, and for a multitude of reasons. The best performing ones such as Morgan Stanley are only now reaching back to their pre-Covid position.

While many large banks have managed to mainly offset their losses through a boom in their investment banking divisions (Barclays recent quarterly report being a clear example), it is also an interesting opportunity to consider the role digital transformation should be playing in this offset. Could the extent to which such drops in profitability, and indeed loss of opportunities for growth, have been avoided through a more aggressive digital transformation strategy in the past?

When we talk about an aggressive digital strategy, we must talk about one that not only caters to employees in ways such as remote working, but one that effectively tackles customers’ needs and changing behaviors. A digital transformation strategy must tailor an organization’s response to crises, changing customer behavior, and broader market conditions.

Step in AI

It is here that Artificial Intelligence can truly be leveraged.

So much of AI is about leveraging the information hidden in the data both you and others hold to its full potential. Data can hold the answers to many hard questions. Can we identify the vulnerable businesses across our balance sheet? Can we more effectively provision capital due to unseen risks in our mortgage loan book? Can we better direct our sales teams to acquire new customers or grow the holdings of current ones?

It is this data that can present a bank with the opportunities for growth, and it is also this data which holds the warnings behind the implications of shocks yet to come.

Digital transformations can take years to accomplish, and rely on continuous stakeholder engagement to maintain momentum. One way of securing this is by achieving early quick-wins. AI is a key way to unlock these wins. Each additional function, process, or system that undergoes digitalization unlocks a new set of opportunities for AI to unlock value.

For example, if a single type of transactional data has been successfully migrated into a strategic data warehouse, impactful advanced analytics capabilities can relatively rapidly be developed leveraging the transaction data to predict a customer’s likelihood to need an additional product or service, thereby enabling marketing to increase conversion rate on their next cross-sell campaign. 

Furthermore, the roadmap of future AI use cases should be an input into the envisioned ‘future state’ that the digital transformation aims to deliver. The breadth, granularity, quality, and timeliness of how data is captured in a digitized process, as well as how it is stored and governed, should align with future opportunities to deeply mine it for insight and enable the development of predictive capabilities.

Are organizations doing this well?

It is interesting to understand where AI is currently being used to good effect. Many organizations are observed starting in areas of the business where modeling has been a constant for many years.

Such areas as fraud, credit risk, e-trading, and next-best-action are the most obvious and common use cases. The primary reason being they offer the easiest path to impact due to the clear ROI and being areas of the business where there is often a presence of knowledge around the mathematical techniques & understanding required in AI and data science.

Whilst these areas can present significant ROI, they are only scratching the surface of the possible applications of AI within the industry. A completely holistic approach to customer behavior through the leveraging of a broad array of datasets (including transaction and interaction data) is a next step beyond standard next-best-action for example.

Managing the risk that is to come

But the truly transformational area for AI, amplified by the current environment, is in the broad understanding of all risk patterns across a balance sheet. It is an area the industry is waking up to, but not yet broadly adopting.

This has been thrust to the forefront of our minds in the Covid era, and as the economic stimulus packages we see globally start to fade away, economists point to more shocks to come. Many banks are now looking at their balance sheets with a troubled eye.

However it is clear that the broader physical and transition risks which pose the threat of a systematic shock to the banking sector have been ever present, Covid has just brought them much closer to home. Climate change is a clear example of this.

Given the current changes in society, for example, can we leverage our datasets to inform us which customers are becoming, or will become, financially vulnerable? Or which businesses are struggling to cope with global changes? And can we do this far in advance of any default event?

If we know this, and our data often does, then action can be taken to mitigate risk. The introduction of capital ratios after the last financial crisis has done much to soften the blow in the current one, whether this is through a restructuring of exposure, conditional loans, or through financial and advisory partnerships - a thorough understanding poses opportunities for change and growth.

Hope for the future - creating an even playing field

The use of AI in financial services offers us many causes for optimism in the future.

From the ability to fairly and transparently provision capital to retail clients due to a clear understanding of economic drivers and behaviors, through to techniques which explain the impact of future climate risks on portfolio’s and thrust investment away from polluting entities - we should have hope in the power of a technology to throw open the drivers in the world around us.

Finding meaning from data and acting upon it is something the financial services industry has done for as long as it has been in existence, AI offers us the ability to do this at speed and with scale - in as fair and unbiased way as possible.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.