RPA bots managing risk? Here's why it works
Robotic process automation (RPA) is an increasingly well-known business enabler that lets users intuitively deploy software bots for knowledge work. This saves time and reduces money spent on tedious tasks. Since RPA bots are API-agnostic, they also enable new types of integrations and other unique value-add opportunities.
Still, many decision-makers approach RPA with cautious optimism, primarily because they're putting faith in bots' abilities to perform work that is precise, of the necessary quality, and compliant with security and governance rules. But contrary to the notion of bots deviating from quality standards, properly configured RPA bots can actually manage risk with the same level of conformance to protocols as humans.
In fact, RPA operates with greater regard for the rules.
The rule of thumb with RPA is that any process that can be quantitatively explained to a robot can be automated. Thus, RPA's strength is that it can mimic repetitive, rules-based processes, particularly for data entry and iterating of business documentation and databases. In this sense, a software bot acts exactly as a human would. It does not change the process - it just repeats it over and over again so a human worker can focus on qualitative, judgment-based work.
The difference is that a bot will not get distracted, skip a step, accidentally press the wrong key or select the wrong cell on a spreadsheet. Its ongoing performance is as reliable and as safe as the configurations that rule its behavior. These are rigidly defined parameters that do not deviate unpredictably.
On the other hand, human error has been cited as a top cause for process breakdowns and security failures for years. As just one example, human oversights are the top cause of data being lost or compromised, according to InfoSecurity Magazine contributor Norman Shaw. In addition to degrading the quality of a process, overlooking just one critical step could pose a security risk and even liability in the form of a breach of industry regulations.
Further, because bots can integrate with a variety of systems, they can quickly perform complex, time-consuming functions such as gathering data from disparate systems for due diligence and risk reporting purposes. This is extremely useful in risk-averse industries such as finance and health care.
RPA is not a hindrance to risk management - it's a facilitator of it.
Policing advanced processes
RPA can also be incredibly useful for keeping advanced, automated processes in check. As business processes and operations evolve, new APIs and custom integrations may be necessary to enable more complicated workflow calculations. This would normally require substantial QA efforts on the part of developers, who must verify the integrity of resulting data outputs. Additionally, they would need to validate compliance with process rules and protocols, and identify deviation from quality assurance standards. Done manually, this would take a substantial amount of time and effort.
However, since RPA is able to fluidly interact with the front-end of disparate systems, it can perform much of the necessary work that goes into testing governance of new and existing infrastructure that will be used in the development of complex processes.
In this way, RPA not only manages risk, but it also helps to drive operational innovation by verifying the integrity of new processes as they're built. This scales back on investments for testing that typically go into business-process automation.
Given this level of enablement, it's no wonder the global RPA market is projected to be worth $443 million before the end of the year. The only thing business decision-makers value more than affordable productivity gains is the assurance that this enablement happens securely and in-step with QA standards. RPA's potential to supply both is unparalleled.