Traditional Robotic Process Automation(RPA) automates processes based on structured, consistent data. Cognitive RPA goes a step further, by enabling organizations to automate processes that include unstructured data sources, including scanned documents, emails, letters and voice recordings. The real power of cognitive automation is that it enables enterprises to automate more complex, less rules-based tasks, and process data performing similar functions, but in widely different formats and layouts.
Avenir Digital’s Cognitive RPA does this by leveraging Artificial Intelligence (AI) technologies such as Optical Character Recognition (OCR), Text Analytics, and Machine Learning(ML) to improve the experience of your employees and customers.
Cognitive RPA gets its name from how it learns to mimic actions performed by humans while executing tasks within a process. Such processes include learning (acquiring information and contextual rules for using the information), reasoning (using context and rules to reach conclusions) and self-correction (learning from successes and failures).
Unlike traditional unattended RPA, cognitive RPA is adept at handling exceptions without human intervention. For example, most RPA solutions cannot cater for issues such as a date presented in the wrong format, missing information in a form, or slow response times on the network or Internet.
Optical Character Recognition(OCR) and image identification Natural Language Processing(NLP) Machine Learning(ML) Extracting Intent and entities Text analytics Sentiment analysis Categorization Classification Voice recognition
In the slightly longer-term, Avenir Digital plan to offer cognitive decisioning or decisioning automation. This means using AI to automate processes that aren’t rules-based, and that have aspects of decision-making presently only done by humans.
The customer feels he or she is instant-messaging with a human customer service representative. In addition, dynamic interactive voice response (IVR) improves the IVR experience, adjusting the phone tree for repeat callers and anticipating where they will need to go, helping them avoid the usual maze of options. Email conversations can also be automated, AI-based automation watching for triggers that suggest an appropriate time to send an email, then composing and sending the correspondence.
OCR technology and machine learning automates the manual handling of invoices. OCR reads the invoices, and machine learning helps the robot become more accurate and learn from exceptions and mistakes, until only a tiny fraction require human intervention
Automating the process of opening a new bank account. The customer receives an online form from the chatbot, fills it out and uploads Know Your Customer(KYC) documents. The form is submitted to a bot for initial processing (Credit score check & extracting data from the customer’s ID document using OCR.) In case of an exception – say a discrepancy between the customer’s name on the form and on the ID document - it passes it to a human employee for further processing. Machine learning monitors and learns how the human employee validates the customer’s identity. Next time, it will process the same scenario itself without human input.
Today’s customers interact with your organization across a range of touch points and channels – chat, interactive IVR, apps, messaging, and social channels. Integrating Cognitive RPA across all these channels streamlines all inbound and outbound customer experience(CX), enabling customers to do more without live human agents. Cognitive RPA abilities enable the automated system to understand the customer’s intent, make sense of the unstructured data associated with the customer, predict behaviour, and then execute a request on their behalf.
OCR and machine learning digitises all output from Imaging (X rays and scans) and Pathology(laboratory analysis of bodily fluids and tissues), collates and categorises and facilitates sharing among specialists in Multi-Disciplinary Meetings(MDMs), allowing better diagnoses and treatment plans.
Handwritten enrollment forms and cheques are digitised by OCR, then collated and passed to CRM and ERP systems by integrated ML/Python system. Manual processing and human error eliminated, and form/cheque processing time reduced by 10x.