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Digital transformation is changing business models, spawning new revenue-producing services, and creating sticky experiences for customers. But no less important are the cost reductions attainable through digitization of manual business processes.

Many companies in manufacturing, healthcare and financial services are tackling the challenge of technology-led transformation. That is, the convergence of digital technologies -- such as big data, the Internet of Things, and artificial intelligence -- with operations to change the way enterprises conduct business.

While new business models, digital-based services, and customer experiences get a lot of attention, applying the tools of digital transformation to mundane labor-intensive internal tasks can add up to huge savings, efficiencies and improved risk management.

This is especially true in financial services, where a few larger companies are beginning to adopt robotic process automation (RPA) -- also known as digital labor –- for manually intensive, rote tasks, the kind that were sent offshore in the past to achieve labor arbitrage.

The potential for cost savings across all kinds of business processes could be greater than the potential for new revenues from digital transformation. We estimate that 45% of all work activities in all industries could be automated through RPA, saving $2 trillion in global workforce costs.

Whether that percentage holds in financial services remains to be seen, but RPA has huge potential in regulatory and compliance reporting, especially for reducing costly errors in that process, and in financial reconciliation, especially for closing the gaps between non-straight-through-processing (STP) systems.

Software bots, not R2D2

Becoming cloud-based and fully digitized can require heavy investment and heavy lifting by the IT staff in a financial services firm. Compared to that, RPA is a walk in the park. RPA is not typically part of the IT infrastructure, but sits on top of it.

RPA can be designed with lightweight, easy-to-program software tools that can automate a range of digital activity. Business analysts and other power users can be trained to create RPA with tools available today; many vendors offer training programs. More vendors continue to enter the market, each with a new take on the technology. It is likely that a couple of them will emerge as the front runners over the next two to three years.

“Robotics,” which conjures up images of Star Wars’ R2D2, may be a misnomer, but it’s the term of art used by people in the industry. RPA is achieved with logic-driven software bots that execute pre-programmed rules on mostly structured and some unstructured data to replace humans who currently bridge the gaps in non-STP systems. Technology available now allows financial institutions to automate many computer-based operational tasks like searching, matching, comparing, filing, and more. This can free staff to do higher value work.

RPA can aggregate data from multiple sources to develop an integrated single view to complete business processes. Labor-intensive repetitive activities that need significant amounts of data processing across multiple applications will benefit from RPA without the need to change existing systems.

At the high end of digital labor is the more cognitively advanced intelligent process automation (IPA). IPA is logic-driven software combined with machine learning that “learns” trends in data and uses statistics to execute tasks based on those trends. Use of artificial intelligence is increasing in areas where a vast array of data processing is required to make decisions while considering the overall context.

In time, IPA could be capable of replacing human reasoning in certain complex, non-linear decision paths. IPA is not nearly as evolved as RPA, but adopters that may be interested in it should get on board first with RPA.

Conclusion

RPA is still relatively new. While RPA introduces a higher level of complexity, it creates significant opportunities to accelerate execution speed, improve agility and enhance process efficiency. And the challenges in achieving RPA in financial services often have little to do with technology, and more to do with executive buy–in, cultural issues, immature processes, and change management capabilities.

If implemented effectively, RPA can be a sound investment. Among the reasons:

- Quick time to market: Automations can be implemented quickly – sometimes faster than “on-boarding” a person.

- Fast payback: Software itself is inexpensive and accretive savings can be significant. Payback can be as quick as six to nine months for certain activities and processes.

- Can scale quickly: Technology can be deployed to address seasonal activities (e.g., data remediation) or periods of high market activity.

- Complement other initiatives: Can be applied alongside other process improvement exercises or location strategy initiatives to increase capacity free-up.

It is fair to say that most financial services firms are looking at every one of their business processes and understands how software can help revenue growth and cost reduction. Cost reduction second, but that does not make it any less important, or any less indicative of the grand convergence taking place.