Author’s note: The use of robotic process automation (RPA) is gaining momentum in the financial services industry. The first installment of this five-part series focuses on the common pitfalls banks encounter when planning for and deploying software bots. The second looks at seven steps banks can take to automate efficiently and derive long-term value from their RPA investment. In the third installment, we look at where banks are seeing the greatest ROI on their RPA initiatives. The fourth installment looks at what banks should be thinking about when moving from proof of concept-based, task-level RPA to overall operations transformation.
A multimillion-dollar commercial loan gets approved in 45 minutes. A voice command to your intelligent personal assistant pays your credit card bill. A trade settlement bot reduces the reconciliation time for a failed transaction from several minutes to a quarter of a second. These examples showcase how banks and other financial services companies are now putting robotic process automation (RPA) to serious use.
To date, cost containment and headcount management have been automation’s main attraction. But now our clients are increasingly expressing interest in using RPA to achieve other business goals – frictionless interaction, customer acquisition, product innovation, compliance and more.
As with any new technology, RPA deployment will present challenges, and through our work, we’ve identified “seven deadly sins” that we see as barriers to fully realizing the full potential value of RPA.
Of course, the first step is understanding why to apply RPA at all. Rather than seeing it as the “flavor of the season” or as a panacea for reimagining and simplifying their legacy processes and technologies, decision makers need to be clear-minded about what they hope to achieve from embarking on an RPA journey.
RPA Pitfalls to Avoid
The next step is to examine the potential hurdles that must be overcome.
- Siloed automation efforts. With vendor promises of drag-and-drop simplicity, a business or operations unit may be tempted to take the lead in RPA deployment. In other cases, IT may claim ownership. Either approach can fall short. The business or operations side doesn’t always understand technology scalability issues, while IT doesn’t fully comprehend business pain points or process optimization opportunities. It’s essential for both groups to join forces and understand automation in the context of the enterprise transformation strategy.
- Poor process selection. When considering RPA, many companies take the approach of selecting a simple process, establishing that the automation works, evaluating the benefits it can provide and determining whether it will scale. This approach may overlook a key question, though: Does the process represent the complexities that the automation initiative might present? It’s important to identify a critical mass of factors and then determine whether automation is the right approach. For example, a process with few touchpoints to web applications and simple data entry will not provide inputs for tool selection. On the other hand, if most processes use virtual desktop infrastructure (VDI) and involve calculations and seasonal volume, then selecting the simplest among these will provide better insight into tool selection and an overall roadmap.
- Insufficient organizational involvement in execution. Leadership may view automation as chiefly a technology play. But if you simply hand off a process to third-party developers for scripting, they’re unlikely to understand key factors adequately – how the process affects other areas of the organization, potential liabilities associated with automation, and whether automation actually supports business goals. The business unit and IT need to coordinate their involvement.
- Underestimation of ecosystem requirements. Automation can exponentially accelerate the performance of tasks such as data entry, straining systems’ capacity to scale and respond to demand. You’ll need to assess and implement infrastructure changes to effectively integrate RPA into the organization’s IT portfolio, as well as add safeguards to ensure robots operate within established bounds. For example, a system designed to handle human interaction may show signs of stress when bots begin executing transactions 100 times faster than a human operator.
- Postponement of planning until after a proof of concept or pilot. It’s essential to choose the right process to evaluate for its automation potential, but a proof of concept is just that – it validates a hypothesis. Waiting until this happens before deciding how automation will be deployed ignores how the solution fits into the spectrum of what is planned. Creating a solid business case for the proof of concept can help avoid this. Such planning extends to security, compliance and people requirements, particularly the skills needed to scale the solution. For example, a basic question of how to manage IDs for bots will uncover changes required for regulatory compliance and security.
- Underestimation of automation’s organizational impact. Views of RPA’s potential impact on jobs range from a bounty of new opportunities to dystopian unemployment. By one estimate, about 60% percent of occupations could have 30% or more of their basic activities automated. However, the future plays out, people will find themselves doing different things and needing new skills. Managing these transitions intuitively and guiding people to prepare for more challenging and fulfilling work can help smooth the shifts.
- Poorly defined success and acceptance criteria. A validated proof of concept can suggest that automation can succeed without human assistance. But automation is not necessarily intended to replace humans. Instead, it can fulfill a higher purpose by providing information that helps people perform their jobs better.
The Art of Future-Proofing
Automation is not a silver bullet. In fact, it’s just one vital component of an overall digital transformation strategy, which should also include elements such as analytics, human-centric design, the Internet of Things and blockchain. That said, automation will be an essential tool for achieving banking and financial services leadership in the future.
By understanding the seven deadly sins of RPA deployment, and deciding in advance how you’ll use it to achieve business goals, your institution can begin to capture the value of this transformative technology.
Our next blog illuminates the right reasons for pursuing RPA, including cost efficiency, compliance, scalability and business transformation. The third in our series looks at where banks are achieving the greatest success with RPA.
Makarand Pande, a Digital Partner and Automation Market Lead in Cognizant’s Banking and Financial Services business unit, contributed to this blog.
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