Nasdaq: AI’s Role in Digital Transformation for Financial Services

Satya Nadella recently said that the Covid-19 pandemic has caused “2 years’ worth of digital transformation in two months”. 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 behavioural 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.

Whilst many large banks have managed to offset much of 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 behaviours. A digital transformation strategy must tailor an organisation’s response to crises, changing customer behaviour, and broader market conditions.

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? The same data that can present a bank with the opportunities for growth can also hold 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 utilizing AI.  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.

It is interesting to understand where AI is currently being used to good effect. Many organisations are observed starting in areas of the business where modelling 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 behaviour 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.

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. Notable instance of where this is being done is the work done by HSBC and IBM on an AI powered US Equities index.

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.

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 behaviours, 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.