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Robotic Process Automation – Overcoming Challenges to Add Day-to-Day Value

Drew Douglas, Head of Global Liquidity and Cash Management, HSBC North America
Drew Douglas, Head of Global Liquidity and Cash Management, HSBC North America

Drew Douglas, Head of Global Liquidity and Cash Management, HSBC North America

Like any company, people are at the core of who HSBC is and what we do. That’s why we’re always looking for new technology to help us and our clients do more, better and faster. For most banks, that’s typically meant creating front-end, easy-to-use digital product solutions. While these projects are still critical, HSBC is turning its attention to the back office as well – looking for innovations that provide frictionless servicing and zero contact resolutions which also minimize the risk of errors.

In the past, improving client service often focused on adding more people or bringing in new platforms. The end result, however, can be an increase in the overall cost of servicing without a significant ROI or meaningful impact on how the client operates. For one thing, teams can become more bureaucratic and less efficient over time as they continue to grow and as end-to-end processes are shared more widely across multiple business units. When targeting large new platforms, long rollouts and integration challenges make it rare for these projects to deliver on their promises in the expected timeframe. They can also often miss the mark in terms of intended effect to the overall client experience.

Emerging technologies, such as Robotic Process Automation (RPA), are changing that. RPA can improve back office efficiency quickly and cost-effectively by automating manual, repetitive and error-prone processes. Once RPA is in place, outcomes can be further enriched with Artificial Intelligence (AI) as well as application programming interface (API) software. The question for many is where to start with technologies like RPA. Following is a synopsis of HSBC’s approach and what we’ve learned along the way.

Demystifying the Role of Rpa

Robotics is an intimidating term for many people. Employees assume right away they will be replaced by robots and clients might feel they will lose the human touch to service. At HSBC, we look at it through a different lens. We use RPA to augment the work we do by putting the ‘bot’ in the middle of a process. The bot takes on monotonous day-to-day tasks that don’t add value to client interactions and relationships – freeing staff up to manage exceptions, focus on more in-depth analysis or work more closely with clients.

Consider Client and Employee Impact First

HSBC’s client-centric business model means everything we do must have a positive impact on our clients. It’s equally important that the effect is a positive one for the immediate team as well as all of HSBC’s global colleagues. That’s why we started with the people who know our clients best and have the deepest understanding of how our processes work.

The first step in our transformation journey was to understand the key process “pain points” in our service teams. To do that, our employees came together to share their experiences and bring opportunities to the forefront, looking at solutioning across processes. Our goal was not to find perfection out of the gate, but to give teams the right tools (analytical and technological) to generate original, disruptive ideas for quickly improving the service environment.

Self-selecting employees then joined small, focused sessions to work on the opportunities they had prioritized – tackling them with an open, problem-solving mindset. As a wider group, they were able to consider common core solution elements that are required across the various identified opportunities. We chose to build out those we felt were repeatable with the fastest time-to-market and reasonable ROI.

Keep it Small and Regional at the Start

The key with RPA, in particular, is not to look for large scale wins, but to go for more minor but better defined successes. Our back office innovation to date has been prioritized on a local basis and focused on simplistic automation and narrow scope. It was also important for us to execute on an agile basis and celebrate small successes along the way to build team confidence.

These smaller pieces of work require less of an investment in terms of money and resources but also deliver a significant impact in a short timeframe. As an example, in 2019, our US team used RPA to automate approximately 60,000 repetitive payment tasks that had previously been handled manually. As our teams became comfortable executing small wins and the art of the possible, more opportunities came forward.

Add Complexity and Scale Over Time

Still on our journey, we have built on our initial learnings, best practices and, importantly, the confidence created in the first transformation wave.

Solving more basic problems with a narrow focus first has provided our staff with the knowledge and skill sets of “The How” to go about transformation. We’re now tackling more complex opportunities across multiple areas and functions. Whether liquidity and cash management, trade or credit –we are now able to leverage the front-end graphical user interface (GUI) or underlying code of previous solutions.

The team has expanded into machine learning and AI wrappers to add functionality such as segmenting customers or analyzing text or voice conversations. In our case, we plan to build on what we accomplished in 2019 by adding AI to RPA to free up more resources and direct them back toward our clients. Further down the road, we can also add third-party, packaged APIs where it makes sense to continue expanding on the value and scope of solutions. Eventually, what starts out with automating a mundane process can turn into a complete redesign – resulting in a more dynamic process.

An Outcome: Bolt

One outcome of our more advanced work is the development of our inbound customer service query system, BOLT. Instead of having people interrogate inquiries, we are using machine learning and RPA software to track a query as it moves around the bank. This way, when a similar issue comes in again, the software will recognize that queries like it often move in a certain pattern. Then, instead of following the entire pattern, BOLT will take it straight to the end-point – getting the customer to the right place the first time and improving performance.

While our commitment to this transformation journey continues and our expertise, understanding and comfort with RPA grows, it is clearer than ever that people are the most critical resource in such change programs.

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