By Matt Allan1
1Founder, Fintech Sandpit
Artificial intelligence (AI) is new, exciting…and difficult. McKinsey & Co estimates the technology will generate $13 trillion of revenue over the next decade, affording global GDP over 1.2%.1 New use cases consistently promise not only efficiency improvements but also deeper experiences for customers. However, the road to AI adoption is windy, unmarked and filled with potholes of resistance. Herein lies the root of the problem. Banks have traditionally placed themselves at the centre of their own universe, while the customer came second. The customer only had access to the bank during opening hours, and always had to play by the bank’s rules. This is incompatible with a successful deployment of AI, which requires an obsession with customers, their data and its quality.2 Many banks are missing fundamental aspects of reliability, scalability and security within their existing architectures, and yet are committing to build AI systems that promise to be reliable for customers, adaptable for the future and secure for data. We must grow legs before we can crawl, walk or run, and banks must create a firm digital foundation before they rush to deploy AI. This chapter teases out some of the challenges that firms must overcome and the opportunities available to maximize the value of artificial intelligence.
As customers began to demand new forms of interaction via internet and anywhere/anytime mobile banking, the requirements placed on legacy core banking systems grew exponentially. Instead of renewing these systems, most banks layered additional products on top of the base system to satisfy new business demands, creating additional layers of operational complexity, cost and risks. Core technologies were expanded to do things they were never designed to do. From a cost perspective, this means most banks today spend between3 80% and 90% of their IT budget simply to keep the lights on. Some banks still use core banking software purchased 30 or more years ago. Forcing these systems to support real-time mobile banking or open banking application programming interfaces (APIs) creates a massively complex architecture that is extremely fragile. Advisory firm KPMG4 suggests that the status quo is slowly changing, and that CIOs are becoming more proactive in solving their data access and operational resilience problem. They know that in order to take advantage of technologies like AI, there is an urgent need to revitalize legacy systems, move workloads to the cloud and open their core for collaboration.5
Banks are struggling to adopt AI because they have not traditionally placed customer data at the centre of their operations. Digital transformation is an attempt to rid themselves of the constraints of their legacy technologies and to re-establish the customer as their primary focus. However, undergoing a full technology refresh while continuing to serve millions of customers is like trying to replace the engines on an airplane while 30,000 feet in the air. Banks are stuck in a classic catch-22 dilemma: their ongoing commitment to serve their customers is preventing them from succeeding in the timely deployment of emerging technologies (such as AI), that would ultimately improve how effectively they can serve their customers. Above all else, customers value service availability and want unfettered access to their money at every second of every day. To complicate matters further, banks must continue to support the needs of an increasingly diverse customer base. Challenger banks have started their businesses with a greenfield approach and are unencumbered by the maintenance burden of old products (like cheques) or legacy account structures. Unfortunately for themselves, incumbent banks do not have that luxury.
Banks operate in a web of social friction that they must skilfully navigate during their deployment of emerging technologies like AI. The best example of this is in the debate of whether to close brick-and-mortar branches. Despite declining utilization and significant cost, closing branches in rural towns often escalates to a social or political issue.6 Despite the resistance, banks must continue to overcome the challenges that prevent their digitalization. The applications of artificial intelligence discussed in later chapters will only be realized if banks can successfully navigate this transition successfully.
It’s time to take advantage. Although banks are playing catch-up when it comes to laying a firm foundation for AI, there are several opportunities banks can take advantage of today in order to prepare for tomorrow.
Banks with an open attitude to data sharing will realize much more value from AI than those who seek to lock their data away. Banks who have adopted an open data or platform strategy like Starling, a UK challenger bank, are generating much more revenue from their data than those who remain closed. Starling’s developer APIs go well beyond that mandated by open banking and PSD2, which enable FinTech companies to solve problems for their users and integrate directly with their platform. Accenture7 found that 71% of banking practitioners believe that organisations that embrace open banking will reduce their time to market, streamline their operational costs, and offer better experiences for their customers. Banks who prioritize their data openness will be in a much better position to consistently leverage AI as opportunities arise.
Never before have firms had access to so much contextual and supplementary data about their customers as they have today. Unstructured data contained in news articles, broker research and/or written documents is becoming much easier to ingest in automated systems due to advances in convolutional neural networks. Firms that can synthesize and mobilize the information contained in health, geographical, transaction, credit and social media data sets will be at a significant competitive advantage. To benefit from data availability, banks must pay close attention to improving data quality. Refinitiv,8 a banking consultancy, found that 43% of banking executives see data quality as the biggest hindrance to benefiting from AI. When it comes to machine learning, the adage “garbage in, garbage out” has never been more pertinent. As future chapters will explore, alternative data alongside AI will be the catalyst to streamline back-office processes for legal and compliance purposes. Banks must keep asking how they can become more efficient, and then use the data available to execute.
It is not the strongest of the species that survives, or the most intelligent, but the one most adaptable to change.
Charles Darwin
AI is an incredible set of technologies, yet most banks are still unable to fully realize its potential. Most are trying desperately to move faster, yet the complexity and cost of “keeping the lights on” continue to hold them back. In nature, as in business, those most responsive to change will survive. Financial institutions that are proactive in taking advantage of alternative data and understanding their own data will be better positioned to capitalize on the new and exciting opportunities presented to them.
At Fintech Sandpit, we are helping financial institutions securely work with fintechs to realise the potential of their data. Banks use our digital sandbox to do proof-of-concepts in order to find the best partner to work with. Get in touch to instantly test any fintech on our marketplace.