When most of us think of AI, we imagine slick, intuitive designs, underpinned by quick, efficient systems all handed to you on a personalised plate. What many of us don’t necessarily think of is hard, cold numbers. Odd really when data is the key foundation
underpinning AI. Artificial intelligence is all about data or numbers, input into systems that are then analysed and summarised to provide a game-changing customer experience across all industries but particularly in finance.
Outside of blockchain, artificial intelligence has long been considered the holy grail for financial services. As an industry steeped in data, the pairing is already perfect, and the benefits are unmistakeable. Whether finance brands want to provide a top-level
customer service chatbot or, on a more granular level, deliver financial services in line with ever increasing regulatory guidance, there isn’t much that AI can’t do to progress the finance industry.
Making AI a Success in Financial Services
Paradoxically, the financial services sector as a whole is being left behind with consumer-centric industries across the globe pulling ahead to expose a chasm between what customers want and the experience that they receive. At a time when the customer experience
is heralded as the sacred vessel through which all things are possible for businesses, making a success of AI in finance is considered crucial.
The modern financial brands that are doing well today have one main thing in common – they are dominated by technology. Challenger banks and contemporary financial businesses are disrupting the industry’s standards and setting the pace for AI and data analytics.
However, in their swift route to entry and with consistent software upgrades, there are still some nuanced implementations to bear in mind.
Near the top of that list is having an accurate understanding and knowledge of the data being used. There are many potential downfalls when inputting data and we have all heard of the horror stories around biometric profiling and the biases that can become
apparent when digitising personal data. The same goes for consumer finances, so scrupulously computerising numbers will be fundamental to testing and training software to learn the value from data and unlock its true prophetic potential.
With that said, before we get to understand the data we must first get the infrastructure right. The lack of architecture designed from the ground up for AI-driven operations means that financial services may struggle to incorporate AI into their operations at
all. Legacy systems are notoriously difficult and expensive to upgrade. At a strategic level, banks are deciding whether to deploy a “rip and replace” or using an integrated approach to connect siloed systems. Nonetheless ultimately, at the core of any successful
AI adoption are the right set of technology skills, well-defined data management and high-performance IT infrastructures.
The Logistics of Legacy
Perhaps the biggest challenge for financial services is that AI is an architectural innovation as well as a component innovation – which is to say, its requirements
extend beyond new technology and ideas, to include joining up old technology and ideas in a different way. Competent AI requires massive amounts of data: this is how it learns how things work, and how it predicts the way those ‘things’ will behave in the future.
For many businesses, introducing the systems to manage this data will mean implementing entirely new computing capacity, alongside innovations like ‘internet of things’ monitoring, to gather the information required.
However, in financial services where information has always been the heart of business, there is the more complex problem of transforming existing systems to communicate effectively with AI. Legacy systems in finance have been developed over the course of
decades and changing existing systems which are currently delivering value is a bigger, riskier job – in a highly risk-averse industry – than starting from scratch.
The Change is Coming
One option for operating with legacy systems in a digitalised, intelligent context is to develop an intelligent mesh, or Data Fabric, to bring together the richness of historical data to the user-friendly interface found in modern systems. The smart data
layer can provide a bridge between existing and new infrastructure which has been designed to deliver the speed-to-value which today’s financial services provider needs.
Essentially, significant architecture changes will expand the possibilities for this sector. The move to cloud computing, with its elastic response to demand that can handle the intensive computation that AI training requires without the capital expense
involved in building that capacity in-house, is a key part of this. While in many ways financial services is a sector already at the leading edge of AI, the availability of architecture which is designed from the ground up for AI-driven operations means that
much more change is on the horizon.
There aren’t many out there who can predict AI’s true potential but what we do know, is that its ability to enhance productivity and efficiency through automation are currently unmatched but only if we can get the data fabric right.