AI, RPA and ML – the three most powerful acronyms changing the face of financial services

The number of enquires in both sectors have significantly grown in recent years – this is largely due to an increase in online trading, claims and service enquiries. And digitalisation has made it even more difficult, with consumers interacting and expecting support at all times of the day, every day. This has created a strain on both industries, and resulted in a significant of number of consumers moving towards more agile (and online) options over the more traditional banking institutions.

 

The good news is that many of these inbound requests - at their core - are repetitive and require little to no individual response. This lack of a hands-on approach makes them a perfect fit for automation, specifically robotic process automation - also known as RPA. In recent years, RPA has evolved to help organisations across every sector drive growth by using data to make smart decisions by themselves. This evolution has ultimately freed time and energy for decision makers to focus on more important business tasks. And because of its impact on the industry, many decision makers have started to consider RPA to be a starting point for integrating artificially intelligent solutions into their companies.

 

Emergence of RPA, ML and AI and their impact

Research by Gartner has estimated that the global spending on RPA software is slated to grow from $680 million to £2.4 billion within the next three years – largely due to the financial services and insurance industries. But that’s not to say this technology isn’t already in use within these industries. According to a recent report, 66% of those within the industry already consider machine learning (ML) and artificial intelligence (AI) to have the largest impact on customer service and retention in their business. It’s even hit some of the biggest brands across the world, including Ikea, which recently announced a new augmented reality app as a part of push towards testing AI and virtual reality.

The reason behind this technological push is not without warrant – AI, ML and RPA all reduce the error rate of repetitive tasks and are having a significant impact on more complex areas such as portfolio management, risk management and fraud prevention. Over the next few years, it’s likely that these technologies will continue to evolve and that all industries will start to see new ways in which they can help automate complex data sets to improve organisational flow. This will not only be true in the case of research and knowledge services, but with execution support as well.

 

Why AI services which draw computing power from the cloud are key 

 

When it comes to infrastructure, the financial and insurance industries are particularly driven towards cloud-based systems. According to a NetApp survey, 86.7% of decision makers within the industry rely on AI services that draw their computing power from the cloud. But why is this the case? The short answer is that cloud-based solutions offer financial institutions immense flexibility as well as the performance power to process high data quality and quantity at a significant speed.

 

Cloud infrastructure also goes one step beyond to enable IT security departments to quickly detect and flag fraudulent transactions – which isn’t always the case with other systems. Armed with AI, this particular set up has even been effective at preventing credit card fraud, which in recent years has exploded due to the increase in online purchasing and transactions. AI within the cloud can also support chatbots, which due to Natural Language Processing, have the unique ability to recognise and answer requests in whichever native language is being used. Each and every aspect of this not only supports customer service, but cuts down on time and financial investments for organisations.

 

How to implement AI correctly to see success 

So, how can an organisation within the financial services sector get started when it comes to implementing AI? While it may seem like a complex undertaking, that’s not necessarily the case. As it stands, more than half of organisations already claim to use the technology in their day to day operations - according to the same NetApp survey. In most cases, the responsibility of implementing AI usually lies with management, but sometimes may fall within the IT department. Over the past few years, many organisations have even gone as far as to branch off to create a completely separate department that’s main focus is the implementation of AI. Either way, what’s important - especially when first making the transition - is for organisations to lean on external consultants that can work side by side with the department in charge to alleviate any concerns around privacy, processes and choosing the right solution.

 

From retail and healthcare, to financial services and manufacturing, AI has already started to make a huge impact across every industry. Its ability to simplify complex data sets into easy to manage and digestible processes has already shown immense value across the board. The high implantation rate of projects within the financial services industry shows that IT decision makers recognise its importance and are very much open to adopting measures that bring them one step closer to their fintech competitors – both on the high street and online.