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5-Automation -the Disruptive force in Banking

Updated: Sep 9, 2020

“The world is currently undergoing “the second machine age” a period where “automation allows for the substitution of many cognitive tasks previously performed by humans at a fraction of the cost of human labour, and with greater precision” . From an economic perspective this is great for business “productivity but for the first time productivity has dramatically risen while wages have dropped substantially”. Our relationship with technology also raises difficult social questions about identity and privacy, political as how technology will be applied to humans and how machines will interact with us, and ethical ones (who is liable for machine decisions, how they exercise freedom?).


While manufacturing is the largest opportunity for implementing automation technologies banking and finance is in second place. The service businesses mostly deliver value via employees. Data analytics forms the backbone of automation because it is now possible to automate complex tasks that can act intelligently with programmable logic. Banking and financial services industry are using a combination of artificial intelligence (AI) and robotics process automation (RPA) to boost global revenues.


AI will be used to help Banking sector to support their business decisions. By the mid-2030s, up to 30% of jobs could be automatable (PWC, 2019)A second wave of automation is Transformational for the industry because promises to improve customer experiences, produce cost savings and increase process efficiency and so future revenues. These are the 3 main business value/ opportunities attributable to using AI in banking. This is the industry that maintains most of the clients’ personal data so automation can be used to personalize customer experience, increase revenues by improving business efficiency and reduce cost. Automation in banking will increase capacity and free employees to focus on higher-value projects. “To capture the opportunity, banks must take a strategic, rather than tactical, approach” (MCKinsey, 2017). The main threats /challenges facing the industry are related to the security of customers’ data that in banking are confidential. The quality, the storage and the protection of these data is extremely important because they will be the foundation of the models that will be created. A second threat is the complexity of the algorithm models that, when trained and tested on historic data, could give results very difficult to understand to humans. Finally, a 3rd risk is the Inherent bias: how do we control that the information going into those models isn't skewed towards a particular outcome/interest (Computerworld, 2018)


The source of business value of RPA comes instead from the automation of repetitive business decision and processes with the use of “structured” data. “The market grew 63.1% in 2018 to $846 million, making it the fastest-growing segment in the enterprise software market and expect it to reach $1.3B in 2019” (Gartner, 2019). Rapid growth of this technology means leading banks to aggressively invest in it because if do not innovate,”they will fail to respond effectively as the disruptive technology dominates the market” .


The banking system is the main adopter because the main opportunity coming from this technology is the replacement of manual intensive/repetitive tasks with specialized software (BOT). Banks’s turnaround time to process requests can go from days to hours and minutes. This means process costs could be reduced (30-70%) freeing time for employees to focus on valued added clients. Designing new processes that are optimized for automated work and reducing human intervention and errors brings anyway new threats. 1) Banks could automate many areas (back office in particular) without a vision, a long-term strategy, and internal technical capabilities. Risk is they won’t have any gain in terms of efficiency and effectiveness; 2) Operational risks: robots do not work, or their maintenance becomes too expensive; 3) Project risks:BOT are used for the wrong use cases and too much expectation from automation. So, RPA risks to create long-term technical debt, rather than overcoming it. Banking could become disappointed with the RPA’s Total Cost of Ownership and look to other products.

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