Covid-19 caused the most significant disruption to Banking since the recession. Sitting as the industry does at the front-line of all economic disruption, executives within central, large and small to medium-sized banks have been forced to ask themselves – how do we successfully navigate the post-COVID financial climate.
In short, disruption came at the worst time for banking, with the industry ill prepared to face substantial challenges in its markets. Even prior to the current crisis, US commercial banks were dealing with a $1 billion reduction in net interest income from the first to second half of 2019, pair this with the impending financial pressures brought on by a pandemic and it’s not difficult to see how a substantial challenge was posed and an alternate methodology had to be devised.
Profitability & credit management, securitization, customer relationships and operational resilience – according to KPMG, all will be negatively affected throughout 2021 and beyond and all of which forces executives and business leaders across the industry to come together to search for the solutions necessary to negate that disruption. Increasingly, the answer lies with strategic automation.
Strategic Automation: Investing in the Future of Banking
Automation has always been a priority within banking but never more so than now. Broadly speaking, a sort of technological complacency has defined the industry since the start of the millennium, but it’s only since the start of the 2020 that many businesses have started to pay for it. Where previously, broad automation projects were a wish list commodity in banking, pandemic pressures have pushed them swiftly towards essential ventures both during 2021 and into the future of banking.
Artificial Intelligence & Machine Learning
There is a clear distinction between AI and Automation, in that Artificial Intelligence refers to algorithms which mimic human intelligence – useful for making decisions from data or recognizing patterns – whilst automation refers to machines autonomously completing repetitive tasks without human intervention. The following are all examples of strategic automation but with key differentiating factors.
Artificial Intelligence: AI is being used in the banking industry to enhance the customer experience, predict trends, and provide realistic interactive interfaces. According to a report by the National Business Research Institute and Narrative Science, about 32% of financial service providers are already using AI technologies like Predictive Analytics & Voice Recognition. In short, AI is only just starting to help leverage human and machine capabilities optimally to drive operational and cost efficiency.
Robotic Process Automation (RPA): is perhaps the most significant example of how the banking industry plans to automate processes. Reports suggested that by the end of 2020 global RPA markets predicted to reach up to $2.9bn, and due to the ongoing needs in the market, this will only increase in 2021. The primary aim of RPA in the banking industry is to assist in processing repetitive banking work, helping with everything from Automatic Report Generation to Customer Onboarding.
Machine learning: ML is focused on developing computer programs that autonomously learn and improve from training data. Top banks in the US such as JPMorgan, Wells Fargo, & Bank of America are already using machine learning to provide customer support, detect fraud in real-time, manage customer data and undertake risk modelling for investments. Through impactful experimentation with each of these technologies, executives are one step closer to delivering the future of banking.
Human Vs Automation
There are however, justified fears surrounding the ongoing push to automation. As an example, a recent Wells Fargo report suggests that strategic automation could replace as many as 200,000 banking jobs in the next 10 years, for perspective, this number makes up 13.3% of the 1.5 million US jobs predicted to be made redundant across all industries in the same period. This obviously represents a substantial loss in headcount and executives should hesitate before committing to laying off vast swathes of their staff.
There is a careful balance to strike between automated and human solutions in any industry but finance is particularly delicate. Whilst it is true that AI can help to improve our view of audiences and sectors through the retrieval and quantification of data, it is currently ineffective when it comes to accurately replicating the human-to-human experience.
Finances are an inherently private matter, and the simple truth of the matter is that most consumers would prefer to talk about these matters with a real person, in fact, as much as 86% of consumers prefer talking to a human over a chatbot. To this end, when considering the technologies an organization uses to bring about the future, executives must first question how it affects their staff and customers and consequently, their image. Only by doing so will executives be able to realize a new system which works for all involved, not just the programs attempting to make sense of the data.
Continue the debate at the Banking Innovation Summit, a GDS Summit, where we bring together senior Banking executives who are actively seeking to share, learn, engage, and find the best technology solutions.
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