Since the implementation of artificial intelligence, machine learning and robotics into the financial services sector, there has been ongoing speculation that jobs will be lost, humans will be replaced, and this technology will cause too much disruption and perhaps a distraction.
However, as time goes on and the reality of what is possible manifests, these technologies are now continuously proving their credibility.
The most exciting aspect of artificial intelligence (AI) is that most companies across the sector have only just scratched the surface with its capabilities. Primarily there are two distinct types of AI, the automated chatbots you experience when enquiring on a website, these are predominately used and known across multiple industries. The other form of AI runs complicated tasks behind the scenes, freeing up time for those who would usually have those responsibilities. The extent of intricate jobs AI can complete is particularly clear in the financial services sector. Large sum transactions, communications between bank accounts and markets, all happen within an instant by an automated AI mind.
Advanced versions of the AI systems we are currently aware off are on the horizon and this means a substantial increase in the need for experienced professionals to not only handle but manipulate the technology to work in sync with a company’s original structure and produce the desired long-term results.
In isolation, however, AI is often still deemed as an expensive investment. Rightly or wrongly.
Machine learning (ML) is an example of new technology that is positively challenging the transition companies are going through. The pace of machine learning development has been accelerated and quite rightly so. It is making the experience for banking customers, for example, more accessible, efficient and tailored.
It is commonly used for anti-money laundering purposes and fraud detection through its prediction and pattern recognition capabilities. In an era where data is one of the highest valued aspects of the financial services sector, the older core banking structures cannot seamlessly handle the increasingly larger and complex data that is being fed into its systems.
Regulations are usually considered a barrier to machine learning deployment, but it is in fact the internal constraints that can come with legacy IT systems and data limitations.
How is it possible for robotic process automation (RPA) to drive value and growth across the financial services and banking sector?
RPA not only offers the element of efficiency when driving value, but it also creates underestimated growth. The industry has experienced a new wave of automation, as a technological tool, RPA is being used across all regulated sectors of insurance and banking, across the front and back office to take over manual activities. It brings the enhancement of human support, along with acting as the integration point between existing legacy structures that are a cause for ongoing concern.
The major limitations to RPA enhancement are the people in an organisation agreeing to a specific process. The debate still goes on across organisations; do you re-engineer the processes to drive efficiencies or do you take a process and automate it. The latter approach generates ROI in terms of allowing more efficient employee time.
What do all three have in common?
Rather than replacing jobs in the financial services sector, this trio of technology will be changing roles, whilst adding potentially hundreds of new positions for highly skilled and experienced specialists. The disruption from automation is evident, and there will likely be a transition period of adjustment as people accept and embrace their benefits.
Those with expertise across development, data science and security will continue to be in high demand in delivering artificial intelligence, machine learning and robotics. Embracing these technologies can in the long-term, future proof a company’s existence, but this investment in change requires forward-thinking leaders who are prepared to take steps forward, big or small.
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