Lindsay McEwan, VP and Managing Director EMEA, Tealium
The culmination of next-generation technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), marks the advent of what Professor Klaus Schwab calls the ‘Fourth Industrial Revolution’. A revolution described as a fusion of the physical, digital, and biological worlds by the Founder and Chair of the World Economic Forum.
For those who adapt, the benefits are huge. Improved efficiency, coupled with a deeper understanding of customers and audiences, will revolutionise the way in which financial institutions operate, in turn having a significant effect on ROI.
In the financial services sector, there are many who already share this vision of the future. Over three-quarters of bankers believe that AI will dominate banking within three years, from streamlining communications to delivering tailored recommendations in real time. In addition, 86% of business leaders in the sector claim they are already using AI to improve operations, and global banks such as JP Morgan are already pioneering the use of AI to predict market trends.
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But the benefits of adopting AI and ML all hinge on the actionability of in-house data. So how can leading institutions ensure they’re prepared for the onset of the new revolution?
Tailor today’s data for tomorrow’s transactions
Today’s digitally savvy consumer – used to the convenience and bespoke service of platforms such as Amazon, Netflix, and Uber – now expects the same level of performance from all service providers – including banks and financial institutions.
By using AI and ML, banks will be able to input key insights into a machine, which will instantly ‘learn’ a customer’s preferences to provide tailored customer recommendations, and enable rapid responses as well as around-the-clock chatbot assistance. The result is a seamless yet personalised customer experience that garners long-term loyalty. But first, institutions need to consider how to maximise the use of these technologies inconjunction with their existing bank of data.
While consumers are generating more data than ever, banks and other financial institutions need to assess whether they are actually in a position to deal with the sheer volume of insight – to make sure each interaction is relevant – before they can apply their algorithms.
Invest in invaluable insight
Due to the fragmented nature of a typical customer journey, the data held on each individual customer is all-too often spread across disparate data silos within the organisation. Most banks operate through multiple customer-facing channels – branches, ATMs, telephone banking, online banking, and mobile apps –so it is imperative the various data points are connected before they are processed through ML. The insights gleaned will only be as good as the data submitted, so it stands to reason that an incomplete, fractured view of the customer will lead to misinformed results and redundant efforts.
A robust data management platform can help assimilate customer data and make sure it’s all in one place, easy to access by those who need to, and above all, actionable – whether by humans or machines.
This is shaping up to be a busy year for the financial services industry in terms of legislation. MiFID II and PSD2 – a fundamental part of the new Open Banking standard – came into effect in January.And with the forthcoming General Data Protection Regulation (GDPR) making it mandatory for businesses to keep their data house in order, it’s a case of now or never for many as they look to survive the revolution while remaining data compliant.
Banks and other financial services companies will need to be in a position to quickly respond to requests from any individual regarding the personal data they hold and how it is being used, so they need to be able to retrieve this data easily. While compliance with these regulations might seem daunting, in reality it presents the perfect opportunity for businesses to take stock of their data management processes and explore the use of emerging technology.
While AI and ML techniques hold a lot of promise for the industry, the key to implementation is solid data. So as we wake up to the dawn of the fourth industrial revolution, it’s time to embrace the new regulations and adopt a ‘back to basics’ data strategy to ensure all of the information held today is ready for action tomorrow.