By Andre Reitenbach, CEO, Gcore
Artificial Intelligence (AI) is a hot topic and enterprises across all sectors are seeking ways to harness its potential. Unlike other recent technology innovations, such as cryptocurrency and the Metaverse, AI is already widely embedded in our lives and will undoubtedly have the most relevance long into the future. Which means that banks and financial services companies must keep ahead of its rapid advances.
Finding ways to thrive and become AI-powered, even after years on the digitalisation journey, means that financial organisations are seeking a balance between achieving the agility and responsiveness of a modern fintech with its ability to serve customer needs, with putting security of compliance at risk, both bedrocks of banking.
A McKinsey report on building the ‘AI bank of the future’ suggested that banks must invest in transforming capabilities across all four layers of the integrated capability stack: the engagement layer, the AI-powered decisioning layer, the core technology and data layer, and the operating model.
To achieve this, they need a fresh approach to solving ongoing infrastructure problems and the good news is that the groundwork has already been done in other sectors. One example is online gaming, in which a single game can attract over a million players simultaneously, demanding an incredibly robust network with minimal latency, high levels of security, and the delivery of massive levels of real-time data traffic.
Evolving AI architecture to the edge
The infrastructure built to meet these demands, by companies such as Gcore, which is well known for serving the gaming market, has now evolved to support AI capabilities in other industries.
To achieve this it is necessary to connect cloud, network, AI and security solutions in one platform. The cloud element, powered by GPUs, allows banks, and other financial organisations such as payment operators, traders and stockbrokers, to reliably train generative AI models, build proof of concept projects and drive their AI innovations. The network can manage heavy data loads with minimal latency, ensuring customer applications run seamlessly in real-time. And stringent security protects against DDoS attacks on websites, applications and APIs.
With AI, however, the moment of truth is when these organisations run the models they have trained, and scale them to meet the needs of potentially millions of globally distributed customers. This process – inference – has been a huge challenge for all companies demanding unprecedented compute power, but the introduction of Edge AI has delivered a solution.
Edge simply means IT infrastructure that processes data close to the end user, and Edge computing is now being widely used to provide users with a multitude of services quickly and securely. Edge AI allows banks and finance companies not just to train their AI models in the cloud but to serve them up cost-effectively and in real-time, relieving bandwidth, reducing latency, speeding up data processes and minimising IT costs.
How AI boosts financial operations
Just two years ago, AI in the cloud was considered a new concept, but in the short time since, the huge demands of AI have expanded technology horizons much further. Building ‘AI banks of the future’, to coin McKinsey’s term, means keeping up with these developments, so it’s important to remember the many advantages that the AI revolution provides.
We are already familiar, for example, with AI-powered chatbots that provide customers with immediate and informed attention around the clock across all channels; AI helps with the analysis of different data sets whether it’s bank transactions and customer payment behaviour or measuring social media sentiment; intelligent decision making allows for rapid sense to be made of insights and financial reports; and task automation such as loans and credit decisions, tracking market trends and assessing regulatory compliance can all be more efficiently managed through AI.
In summary
Every aspect of banking and finance, from biometrics and fraud detection to stress testing plans for expansion into new geographies will intersect with AI sooner, rather than later. The faster that organisations can make infrastructure changes across their IT stacks that equip them for an AI future, the faster they can ensure their customers’ expectations are met, regardless of their location. It is only by doing this that they can hope to compete in the increasingly AI-driven world.
