Insurance companies’ robotic process automation programs are maturing, but remain about a year behind other financial services sectors in widespread adoption. That’s according to a new survey by PwC, which analyzed the current implementation efforts of 65 carriers, banks and capital market firms to determine the future scalability of the technology.
Tier-one P&C and life companies, however, tend to have sizable RPA programs in place, says Kevin Kroen, PwC’s U.S. financial services digital labor leader.
“But if you go a tier down, we’re still seeing companies having internal conversations about what to do,” Kroen added. “Insurers are mixing and matching different tools [emerging technologies], as there is no perfect solution in this space yet.”
Despite its importance to core processes, PwC does not view robotics as the endgame for automation. Rather, the technology is one tool in a larger toolkit of available software; one where robots gather data without much intelligence required to carry out functions. By embedding machine learning and artificial intelligence tools, existing platforms can become fully intelligent, Kroen says.
Financial services firms are now moving on from initial proof of concepts to leverage advanced technologies that complement pilots and produce quantifiable benefits, according to the researcher. Organizations are also working to scale RPA across all business units, with intentions of automating areas that are highly dependent on human labor such as core, claims and finance. PwC calls this stage “RPA 2.0.”
“Getting to scale has proven to be tougher than expected,” said Kroen. “Getting from zero to five bots is easy, but going from five to 100 presents unique challenges.”
These challenges include a lack of knowledge by companies in knowing when and where to apply robotics. Also, how to conjure up an execution model that will both automate basic functions and be user friendly. A third obstacle for companies lies in inconsistent resources and funding, cited by 23% of respondents, according to the report.
The real story is one of successfully plugging RPA into your existing environment and making it work. More than half of those who answered oursurvey said they’d faced some issues with integrating their RPA platform with other surrounding software systems. If you’ve set up a bot to assemble a reconciliation report, you need to give it access to all the source data from multiple systems. Getting and maintaining that access sounds like it should be easy, but it’s often not. This can also introduce all kinds of technology integration, change control, and other risk and control issues, so you really need to think them through ahead of time.
These issues—risk, politics, budgets, silos, quality, and so on— can all be managed. You just have to anticipate them and plan to work around them.
Survey takers also noted some other issues, such as production support model, process design issues, and so on. All these potential pitfalls are manageable, and the companies leading RPA adoption have been aware of them from the start. The important thing is to gain an early understanding of the problems and plan accordingly.
The next three to five years will likely provide a solution to these problems, Kroen says. He expects RPA will reach a larger mass of people due to cheaper costs, convenience and the entrance of new startup players in the market expected to accelerate the evolution of intelligent automation. Additionally, through a concept called “process discovery,” AI and machine learning will be able identify points of automation and create processes for companies.
“In other words, robots creating other robots,” he concluded.
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This article was originally published on dig-in