RPA - Understanding Robotic Process Automation

All the rave these days is around artificial intelligence (AI) as the technology continues to shake up the C-suite while gaining a seat at the table. For C-suite executives however, robotic process automation (RPA) is panning out as the most exciting tool in their digital transformation toolbox. With new compliance regulations looming large, organisations need RPA to help unlock the hidden value in both structured and unstructured documents. Highly regulated industries that control large volume of data and continuously strive for improvement stand to gain most from cognitive automation, which brings intelligence to RPA.

What is RPA?

RPA is the application of technology that uses software robots to interact and perform specific tasks within existing applications, much like employees do for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. The value of RPA has been driven by its ease of use and the quick ROI that can be achieved. Both business and technical users can build “robots” without the need for costly and time-consuming coding. Like most technologies, there are various levels of capabilities available to meet various needs. With RPA, there are three levels: basic, enhanced and cognitive.

Basic RPA focuses on automating simple, repeatable, rules-driven processes, providing recorders to capture and imitate what a user does; extracting data from application screens, workflow automation, Visio-type building blocks and reusable components; and using robots to execute those processes independently or as part of a larger business process. The robots eliminate the “swivel chair” processes for data entry commonly seen in banking, insurance, logistics and transportation, as well as in finance for invoice processing.

Enhanced RPA addresses automation of processes that often involves less structured, more specialised content. Tools and platforms supporting enhanced process automation offer some capabilities such as out-of-the-box, built-in knowledge; an understanding of natural language; ability to consume and leverage unstructured data; automated learning capability; pattern recognition; and e-bonding capabilities to other well-established software platforms.

Cognitive RPA combines advanced technologies such as natural language processing, artificial intelligence, machine learning and data analytics to mimic human activities such as perceiving, inferring, gathering evidence, hypothesising, reasoning and interacting with human counterparts. Envision the capabilities in self-driving vehicles where systems are taught rather than programmed, a process that can take months to years depending on the complexity of the problem domain. While Cognitive RPA requires the largest investment in time and dollars, it also has the greatest potential to transform.

RPA could be the best answer for companies falling behind with compliance for the EU’s General Data Protection Regulation (GDPR). Incorporating RPA with intelligent capture solutions will help organisations derive content analytics to gain insights into content. The following are five specific ways content analytics can aid in GDPR compliance efforts:

1. Identifying Meaningful Insights: Organisations must be able to identify key pieces of information within every document, file and object analysed. It can include any terms that have specialised meaning or significance with the domains (for example, terms related to protected classes in human resources documents). The advantage of automated processes enabled by RPA and intelligent capture is real-time access to information in an easily traversable, hierarchical map, and the system is proactively extracting, classifying and presenting critical insights to the user.
2. Establishing a Record Policy: In the normal course of business, organisations capture data containing personally identifiable information (PII) and need to recognise procedures to ensure they are compliant. RPA and intelligent capture solutions can auto-classify a document and extraction data and apply a record policy to help meet GDPR. If PII data does not meet an established policy, an alert could be generated to intercept and resolve the issue.
3. Aggregating Content: All content within an organisation should be accessible in aggregate form based on rules-based terminology. Standardising terminology across input sources enables content repositories to connect; this is especially helpful for disparate source types.
4. Event-Driven Aggregation: Content relevant to a specific internal or external event – such as a merger or a sale to specific customers – can be aggregated in real time. This is especially useful if an organisation needs to look into fraudulent activity.
5. Tracking and Comparing: Users can track changes to a unit of content and compare versions or different content files with respect to specific concepts or clauses.

GDPR impacts nearly every business unit in the enterprise and nearly every contract, including financial, legal, procurement, human resources, sales and marketing, making the accessibility of the data contained within these contracts crucial to compliance. With GDPR in place, organisations should consider investing in technology that leverages RPA and intelligent capture to gain content analytics.

This article by Anthony Macciola originally appeared on ABBYY.com. Inpute are authorised ABBYY partners and our team of professional services consultants are ABBYY certified.