November 15, 2019
What’s Beyond Robotic Process Automation
RPA is a primitive technology and represents only a small part of what’s needed to scale and allow for straight-through processing.
The insurance industry, which relies heavily on repeatable processes, is embracing robotic process automation (RPA). Gartner projects that global RPA software spending will reach $2.4 billion in 2022. But organizations need to understand that RPA is a primitive technology. And it represents only a small part of what’s needed to scale and enable straight-through processing.
What businesses need for end-to-end automation is an integrated automation platform (IAP).
RPA Is Very Basic – And Does Not Know How to Learn
Bots based on RPA can open spreadsheets and databases, copy data between programs, compare entries and perform other routine tasks, the Boston Consulting Group says.
But BCG adds that “RPA is a Band-Aid.” The firm explains that RPA can lead to a proliferation of spot fixes that threaten IT architecture and integrity.
BCG also notes that RPA bots don’t get smarter with time and experience. “When rules conflict with reality or when unexpected events occur,” the firm says, “a human needs to intervene.”
As a Result, RPA Greatly Limits What Organizations Can Automate
Insurance and risk automation companies currently use RPA as a data transport layer. That involves taking data from structured input sources and bringing that data to a target application by employing robot software.
This is a simple task that doesn’t involve any exception handling. But there’s far more to do.
Insurance companies and other organizations also need to analyze, contextualize, enrich, read and understand their data. However, insurance and risk processes are often complex and involve using data from various varied, unstructured formats and sources.
With unstructured data come multiple exceptions, which require cognitive ability and intelligence to bring out meaning and insights. This is where RPA fails.
RPA falls short because it is hard-coded and rules-driven. RPA is unable to scale and adapt to these more complex unstructured processes. When organizations need to use unstructured data – which has not been prepared or contextualized, or changes in target applications and sources – to power their automation efforts, RPA-based bots just don’t work.
That’s Why Now Is the Time for Insurance Organizations to Embrace IAP
To enjoy the benefits of straight-through processing, businesses need RPA, data availability and data usability. Yet RPA does not deliver these last two functions. Meanwhile, IAP does it all.
The data availability feature of an IAP solution ensures the data is made available and is accessible for automation. It includes technologies such as document classification and indexing, image pre-processing and machine vision for digitization.
Data usability – which an IAP solution also supports – makes sure the available data is ready for business processes. It prepares the data using business rules; data certainty; enrichment lookup; and natural language generation, modeling and processing.
IAPs Bring All the Automation Functions Businesses Need Together
Businesses can buy point solutions from separate vendors to address each of these functions. But working with multiple companies and systems needlessly creates complexity. It entails multiple contracts and integration efforts. And it leads to finger pointing when problems arise.
See also: How to Automate Your Automation
That’s why insurance and risk management companies are looking to IAPs. They automate end-to-end business processes quickly, easily and in a scalable manner.
With IAPs, insurance companies can read and interpret data from unstructured documents – whether those documents are printed or handwritten, inferred or image data. Organizations using IAPs benefit from automation processes that grow smarter over time. And businesses that implement IAP solutions can leverage multiple technologies to drive data velocity to enable optimal business and customer outcomes faster.