Leading financial services firms are turning to intelligent automation (IA) to solve some long-standing problems. But unlike predecessors like robotic process automation (RPA), IA isn’t a single tool. Rather, it describes a collection of automation tools that can solve more sophisticated problems. In fact, financial institutions already use many of these tools independently. Despite challenges, there are practical ways to get started. The payoff—true, end-to-end automation—could be significant.
In many ways, RPA has now stopped being exotic. All kinds of financial services firms have now made serious commitments to implementing the technology, and early adopters have started to see quantifiable benefits in their programs. But RPA is, by its design, fundamentally restricted in its capabilities, and firms will need more intelligent tools to achieve their end-to-end automation vision.
Leading firms have been trying several approaches to develop and implement IA technology. Some are building new development models to drive down the cost to “define and deliver” (citizen-led automation). We see some firms turning to technology to find areas that are best suited for IA (process intelligence). And some have been adopting software to deploy, manage, and maintain automation technology at scale (automation service management). Leaders are also cutting through red tape to allow IA tech to succeed.
Even the automation leaders have struggled a bit to get support for their advanced initiatives, while managing expectations of automation against the actual results. It doesn’t help that the road to RPA implementation has been longer than software manufacturers initially led their customers to expect. Scale, and internal processes, has been a big issue for almost everyone. Some firms struggle to make good, fast decisions about which technologies to use and what the potential payoff might look like. And some find that bots themselves need more maintenance than originally expected. This isn’t unusual, but without expertise in emerging technology, it can be difficult to create a sustainable support model.
One good way for many firms is to start branching out: look into intelligent data extraction (IDE) technologies. Put simply, IDE uses technologies such as optical character recognition to turn data into more useful forms to automate a broader range of activity. Other practical steps: developing a clear digital strategy, beginning a citizen-led automation program to add to your firm’s automation knowledge base, and incorporating risk and controls from the start rather than remediating later.
Intelligent Automation (IA) is revolutionizing Capital Markets Operations. PwC has proven experience bringing custom IA solutions to clients in the industry and can help your firm accelerate its IA journey.
End to end automation utilizing RPA, Optical Character Recognition (OCR), and Natural Language Generation.
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Principal, PwC Intelligent Automation and Digital Upskilling Leader, PwC US
Director, FS Advisory, PwC US
Director, FS Advisory, PwC US