As long as you can solve the data access problem and establish robust governance, there is no reason financial services organisations cannot be out in front on Everyday AI says Sid Bhatia at Dataiku.
UAE GDP growth was 8% in 2022, almost double the 2021 figure, according to the nation’s central bank. A strong driver of this was the BFSI sector, which saw 11% growth in total assets to reach a value just shy of $1 trillion. Technology, and the leverage of the golden goose we know as data, has lent a helping hand to the sector’s players.
This is hardly surprising, as financial services companies have been fuelled by data since the days of paper and pencil. Know-your-customer, credit analysis, liquidity management, and others have been around for years, just waiting for AI to come along and fine-tune their accuracy.
A KPMG UAE banking perspectives report from this year pointed out the creation of new jobs in the financial services industry in areas such as fintech and data analytics. In a section on global factors affecting the cost of risk management, the report referred to early warnings using analytics.
In an analysis of multi-cloud strategies being employed by financial services entities, KPMG cited data analytics as one of the key technologies driving institutions into the cloud. A wave of innovation is already in full ascent, but transformation programmes are not without their challenges.
It all begins with data. Data on customers, data on markets, data on products, data on transactions. And as industry consolidation has stitched together disparate brands – Abu Dhabi Commercial Bank, Union National Bank, and Al Hilal Bank become ADCB Group; National Bank of Abu Dhabi and First Gulf Bank become FAB; NBD and Emirates Bank become Emirates NBD; and so on.
The resultant companies have had to homogenise their data and smooth out their legacy processes to ensure those that need access to data can get it. But they have also had to contend with local privacy laws like the UAE’s Personal Data Protection Law of 2021 and the EU’s GDPR. And they have had to consider the strain on infrastructure and the absence of central warehousing for data.
The best way forward has been to create secure staging areas for the testing of analytics models that are monitored through strong governance. Over time, data can be categorised by type and criticality, from which we can derive its priority in backup and recovery, and its level of sensitivity, from which we can derive the roles that can access it.
Once we have established data access and governance, we must address skills, the shortage of which is a major obstacle to overcome in any analytics programme, even in the financial services sector, where data literacy levels tend to be higher than average. But not every employee is a data expert, and business-embedded analytics, what one might call Everyday AI, calls for an update in mindset, followed quickly by upskilling and change management.
Everyday AI means AI, every day. Every employee thinks about data, how it is collected, how it is stored, how it is accessed, and by whom and for what purpose. Everyday AI means everyone knows what to do in the case of, say, missing data. Do you use proxies or estimates or something else?
Missing prices for traded instruments on a particular day may be suitable for estimates, which can be used to guestimate margin calls and risks, for example. But in other cases, estimates could have an adverse effect on decision making. Employee training, collaboration, between risk experts, domain experts, and data scientists, and governance will make the difference when such calls have to be made.
As the financial services industry’s AI journey moves beyond investment teams, first to the plate, because of their ongoing quest for market insights, banks carry with them important lessons regarding AI’s usefulness. Institutions have discovered that the AI toolbox can help with more than just predictions and new business models.
In fact, successful Everyday AI organisations often start with quick wins and move on from there. These areas often include customer analytics and experience enhancement, and process optimisation.
Today, we see a lot of AI leverage in risk management. Regulatory reforms are ongoing in the UAE, and AI can play a key role in many areas of concern. It has the potential to improve trust in the system by bringing agility and impact to investigations involving financial crime and facilitating the introduction of new internal controls.
AI can allow organisations to take significant leaps forward in risk assessment. In years gone by, risks looked a lot different than today’s challenges, where issues such as climate change have to be factored into a wide range of business decisions. Alternative data and agile modelling can be significantly beneficial when navigating crises such as pandemics and climate change.
As the region’s traditional financial services companies face disruption from neo banks, fintechs, and crypto services, AI can help to dazzle customers enough for them to reconsider jumping ship. Today, competitive landscapes are dominated by those who understand, and can move with, customer demands. Personalisation is a natural deliverable for AI, and with thoughtful design, it can be leveraged to provide seamless self-service experiences.
Knowing the customer is about much more than AML. When that knowledge is deployed throughout an engagement session to make the customer feel appreciated and valued, it leads to the building of meaningful rapport.
When Everyday AI is in place, financial services business has multiple opportunities within reach. These technologies are already migrating from data labs to real-world business operations. The only question that remains is which organisations will be the last to move.
As long as you can solve the data access problem and establish robust governance, there is no reason your financial services organisation cannot be out in front on Everyday AI, satisfying regulators, delighting customers, and boosting the bottom line.
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