CASHING IN
collected or ‘ pulled ’ from our interactions with Internet-of-Things ( IoT ) sensors we encounter in our daily lives .
As a result , the data centre is rapidly changing to no longer be the centre of data . The need to handle , manipulate , communicate , store and retrieve data efficiently is moving processing capacity closer to the user than ever before . This phenomenon is known as ‘ data gravity ’ and draws the physical location of digital infrastructure closer to the data source itself . This creates a new set of challenges – and opportunities – for financial service organisations .
Changing the competitive landscape
Financial firms are not only adopting cloud-based technology to deliver a much better service for their clients , but they are doing so to remain relevant . Artificial Intelligence ( AI ) is one such example . For processing simple , repetitive tasks or extracting insights from large amounts of data , AI applications , in combination with Edge Computing , have the power to create significant competitive advantages .
Pascal Holt , Director of Marketing , Iceotope
McKinsey & Company estimates that AI technologies could potentially deliver up to US $ 1 trillion of additional value each year for global banks . They found that AI could ‘ help boost revenues through increased personalisation of services to customers ( and employees ); lower costs through efficiencies generated by higher automation , reduced errors rates and better resource utilisation ; and uncover new and previously unrealised opportunities based on an improved ability to process and generate insights from vast troves of data ’.
A more personal customer experience
Technologies like AI , Machine Learning ( ML ) and Natural Language Processing ( NLP ) utilise the cloud but require Edge Computing for processing data closer to where the data is generated . For traditional retail banking firms , that creates an opportunity to improve customer service while reducing costs .
Going back to our ‘ push ’ vs ‘ pull ’ discussion , retail banking has historically been very much in the push category . All
customers are given the same product information , regardless of whether it is relevant to them or not . With Edge Computing , the data gathered helps the bank better understand individual financial needs enabling them to customise advertising and product offerings accordingly .
HSBC is taking this type of customisation one step further with Pepper , a semihumanoid robot , operating in several branches in the US . Pepper uses NLP to interact with customers . The data intelligence needed for Pepper to successfully and beneficially engage with human customers also requires real-time , low-latency analysis of large quantities of data . All of which are easily served through Edge Computing .
While Pepper may be a fun way to engage with customers , there are plenty of other use cases for Edge Computing in banking and financial services . Security and fraud detection / prevention are critically important as unauthorised financial fraud losses across payment cards , remote banking and cheques totalled £ 360.8 million in H1 2022 , according to UK Finance . There is also a significant regulatory burden on modern banking and Edge Computing enables real-time monitoring of
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