Cognitive robotic process automation or cognitive RPA is a subset of robotic process automation (RPA) that uses artificial intelligence (AI) technologies, including machine learning (ML), optical character recognition (OCR), and text analytics, to automate work processes.

In essence, it is a highly advanced form of RPA wherein robotic or automated processes highly mimic human activities. Most of the functions carried out by cognitive RPA systems focus on learning (gathering information), reasoning (forming contextual conclusions), and self-correction (analyzing successes and failures).

Compared with traditional RPA, which requires structured data, cognitive RPA can automate processes given unstructured information, such as emails, voice recordings, letters, and scanned documents. It can process data without human intervention, removing complex but non-rule-based tasks off human workers’ hands.

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Cognitive robotic process automation

Cognitive Robotic Process Automation Use Cases

In recent years, cognitive RPA has attracted many users because of its immense capabilities. Some of its users include:

Insurance Industry

The insurance industry uses RPA with computer vision. Computer vision allows a computer to understand digital data for processing. It can read information from any screen with the help of AI.

Insurance companies, particularly those that provide auto insurance, can use RPA with computer vision to assess vehicle damage during claims assessment. That removes biases and standardizes the process because cognitive RPA systems can function with minimal human intervention.

For paperwork, insurance companies can use AI-powered file extraction to process both structured and unstructured data.

One example of a cognitive RPA used in the insurance industry is Gleematic. It uses optical character recognition (OCR) for customer onboarding. By scanning identity cards and filled up forms, the cognitive RPA system automatically sends information to storage systems.

Retail Industry

Organizations can improve customer service support with a cognitive RPA system capable of natural language processing (NLP). It can derive information from free-flowing text commonly seen in chats and emails. Such a system can further be enhanced to carry out end-to-end automation of customer service support with sentiment analysis, speech-to-text capabilities, and intent and entity classification.

For example, a cognitive RPA system with NLP functionality can gauge an email writer’s intent. It can either reply or forward the message to the appropriate department.

Another method would be to use cognitive RPA in chatbots. That would improve chatbots’ capability to enhance interactions with users. The chatbots can even be made to perform tasks that enhance engagement, such as making product recommendations, changing delivery dates, or canceling orders. In essence, human customer service tasks are streamlined.

UiPath is a widely used cognitive RPA in the retail industry. It can do sales analytics and order processing through the implementation of cognitive bots.

Financial Services Industry

Cognitive RPA systems with predictive analysis capabilities can perform many statistical analyses, such as predictive modeling, ML, and data mining. They can also analyze historical and current facts to make predictions. Combined cognitive RPA and predictive analysis would allow financial institutions to automate anomaly and fraud detection. The system can gather data from past user transactions and feed it into analysis systems. It can also automatically report instances of fraud to relevant authorities.

IBS Insight is a useful cognitive RPA software that can help firms in the banking industry prevent fraud. Watch the video “ACH Stop Payments in Mid-Sized Banks – FIS Insight, ACH Tracker,” showing how it automatically stops payments when fraud is detected.

Cognitive RPA removes the burden of performing repetitive and tedious tasks, particularly those that require a high level of intelligence, from human staff’s shoulders. It makes it easier for organizations to streamline insurance claim processing, carry out end-to-end customer service, and process financial transactions.