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PwC’s report ‘Top Financial Services Issues of 2018’ evaluates the themes that will define 2018 for the financial services industry. Among the various themes, the report indicates a shift from Robotic Process Automation (RPA) to Intelligent Process Automation (IPA).
“Today’s bots rely on humans to train them, but this will likely change. In 2018, expect to see emerging applications of IPA, including machine learning, auto process discovery, and natural language processing. While these advanced tools still need to be trained, they can learn from prior decisions and data patterns. Many of our clients tell us they are exploring IPA, have IPA bots in production, or are looking to scale,” the report states.
According to PwC’s 2017 Financial Services RPA Survey, 15% of financial institutions are actively planning or executing live projects around IPA and 9% already have IPA bots in production. Another 9% organizations are planning to scale their IPA systems. The fact that 21% organizations are experimenting with IPA and another 27% are in education process, is an encouraging sign of future IPA adoption among financial services companies.
What is IPA and how is it different from RPA?
In layman’s term, IPA is RPA moving to the next level of evolution in digital labor, in terms of intelligence and sophistication as it means automation enabled by ‘context-aware’ robots. Context-aware is the key here as that’s what essentially differentiates IPA from RPA. IPA stands at the higher end of digital labor, wherein robots can learn from prior decisions and data patterns to make decisions by themselves.
As McKinsey puts it, IPA “takes the robot out of the human”.
In its report ‘Intelligent process automation: The engine at the core of the next-generation’, McKinsey describes IPA as follows:
At its core, IPA is an emerging set of new technologies that combines fundamental process redesign with robotic process automation and machine learning. It is a suite of business-process improvements and next-generation tools that assists the knowledge worker by removing repetitive, replicable, and routine tasks. And it can radically improve customer journeys by simplifying interactions and speeding up processes. IPA mimics activities carried out by humans and, over time, learns to do them even better. Traditional levers of rule-based automation are augmented with decision-making capabilities thanks to advances in deep learning and cognitive technology.
According to McKinsey, in essence, five core technologies come together to form IPA. These five technologies are RPA, Smart Workflow, Machine Learning/Advanced Analytics, Natural-Language Generation (NLG) and Cognitive agents.
Here’s an example by McKinsey of how IPA can put these five core technologies into action in an insurance company. A human claims processor pulls data from 13 disparate systems to provide the service. With IPA, robots can replace manual clicks (RPA), interpret text-heavy communications (NLG), make rule-based decisions that don’t have to be preprogrammed (Machine Learning), offer customers suggestions (cognitive agents), and provide real-time tracking of handoffs between systems and people (smart workflows).
Factors driving the shift from RPA to IPA
While RPA is making significant improvements into company’s operations by replacing rote human activity with automated tasks, organizations are realizing that RPA is not enough as it has certain limitations. AI research company, Cognilytica, points out some of these limitations:
– RPA tools get stuck when judgment is needed on what, how, and when to use certain information in certain contexts.
– Traditional RPA tools tend to get tripped up when things deviate substantially from what has been recorded. In particular, there are times when the context of the page needs to be understood, and different actions taken depending on understanding the circumstances.
– There are many times when information is incomplete, requires additional enhancement, or combination with multiple sources to complete a particular task, which RPA cannot address.
IPA can address these limitations through leveraging Machine Learning, Natural Language Processing and other AI approaches. Though, this will also raise further questions on job eliminations as IPA will eliminate many of the exceptions that today require human intervention in RPA systems.