The GCC always learns its lessons well. Since the 2008 financial crisis, regional governments have reformed their FSI sectors to establish greater transparency and stability. Everything from tightening liquidity rules to broad digitalisation of the industry and even the greater focus on ESG can be tied to central banks’ desire to never again be at the mercy of a global crisis.
While challenges such as currency pegs and inescapable market connectivity remain, strides have been made towards a sustainable, resilient regional FSI industry. The fintech sector is humming with activity. For example, in December, Saudi Arabia’s BNPL (buy now, pay later) success story, Tamara became the kingdom’s first fintech unicorn, reaching its billion-dollar valuation during a $340-million Series C equity funding round. And as smaller players soldier on, showing everyone else what is possible, even veteran brands are looking for ways to do more, preferably with less.
Lately, the “do more with less” proposition has inevitably led to generative AI. With all the swagger of a Hollywood starlet, it strutted into the mainstream practically overnight and showed us what modern technology can now do (cheaply) for those who have data. And FSI entities have lots of data. Now, if they can only rest their adoption strategy on the right pillars. Here are the four I would suggest.
Prepare, prepare, now go
Clean your data. Organise your data. Train your people and determine who will have access to what. Establish governance policies. Draw up a roadmap of priorities that includes any necessary cloud migrations. What KPIs will you use? How will they be measured and tied to goals to tell you whether you are succeeding or failing?
All of this goes together to form the horse on the AI journey. The cart, full of AI models, comes later. Without preparation, most complex endeavours are doomed to fail. That said, the preparation should not stall the work. FSIs already have a strong data gathering and analysis mindset that permeates the workforce. And it benefits nobody to spend all your time feeding and grooming the horse while the cart sits idle. So, do not reinvent processes for the sake of reinvention. As you move along the road, everything from the design of workflows to the tolerance for risk may change. You may bore the precious talent waiting to innovate if you spend too much time planning. So, yes, plan diligently, but then get on the road.
Spin plates
Banking and risk go hand in hand. And modern risks are appreciably higher than ever. Institutions must protect privacy and their proprietary interests. Data, analytics and AI all have direct bearings on regional FSI organisations’ reputations and their obligations to regulators. But again, we must be mindful of the implications of a stationary cart. Banks must be daring enough to act but be cautious enough to do so safely. Your people are your innovators, so they need access to data. Ownership must be granted under the right framework and IT setup. Teams must learn to balance action with safety—how to spin plates. They should test, evaluate, and learn from results instinctively while understanding the goal they are pursuing. For example, anti-money laundering (AML) is an obvious target for AI, with clear benefits. Still, an inaccurate model could lead to a false positive and, if managed ineptly, could result in a damaged customer relationship at best and widespread brand excoriation at worst.
Nail it down
At some point, it is time to stop testing the water and commit to a swim. The goal of everyday AI is a culture change, which requires embedding technology in everyday processes. Workflow owners must be empowered to drive their change, albeit in consultation or collaboration with others. Indeed, it is these traditional silos that so often stall progress on AI journeys. However, if culture change has been achieved, all stakeholders will know the metrics, goals, workflows, and governance restrictions. This interconnected, collaborative ownership of projects is a path to success but is only possible after the AI culture has been nailed down.
Give the new kid a shot
Generative AI is as much a potential boon as it is a bane to FSI entities. While the privacy downsides of certain products may rule them out as adoption targets, the raw technology is extremely powerful for meeting banks’ content-production needs. Costs will plummet while the potential for scalability skyrockets.
Some FSI organisations have been attracted to generative AI because of its low data dependency. It also can be a virtual assistant to customer-facing human agents, boosting their real-time performance in many ways, from proactive information gathering to upselling and cross-selling opportunities. Generative AI can support urgent operational issues outside the customer arena, such as sustainability. It can sift through thousands of documents and come back with insights on how portfolios are affecting carbon-impact goals. Generative AI has a prominent role in the FSI sector’s digitalisation. Its applications are extensive, and any player must evaluate it to avoid being left behind.
The road to everyday AI
Horses and carts aside, it is the journey that matters. Every milestone passed and every project delivered is another step towards the data culture that sets a bank apart. Customers want individualisation. They want quick turnarounds on applications and requests for information. And they want security. AI can be an analyst of markets, a valet to customers, and a guard dog for data. Generative AI may be monopolising the limelight, but no matter which you choose, plenty of tools can give regional businesses a leg up, an eye on the horizon, or a fresh new voice.
