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Achieving End to End Automation

· 3 min read
David Seal

Billions of documents, files, and data move between businesses, their customers, and systems each day. Whether it’s invoices, application forms, or contracts, these documents contain mission-critical information that must be correctly extracted and entered into a variety of enterprise systems.


Typically organisations have relied on a combination of manual data entry and outdated technology to classify and process these documents, resulting in data errors, slow processing times and high costs. Despite this, several solutions have emerged to automate these processes. The explosion of Robotic Process Automation (RPA) and data extraction driven by machine learning is changing the way organisations operate, reducing administrative burden and freeing up resources to focus on higher-value activities. RPA works well for processes that are rules-based and involve structured data outputs. RPA solutions automate the highly repetitive tasks that stifle productivity and reduce the risks associated with human error.

As a complement to these rule-based approaches, Machine Learning can perform specific tasks without using explicit instructions, relying on patterns and inference instead. These models then learn and re-train in response to the data it consumes. Machine learning therefore unlocks a world of possibilities for data extraction and has lead to the development of intelligent document processing solutions.

Together as one

While RPA and Machine Learning are each valuable on their own, together they are unstoppable. Despite increasing system connectivity, there will never be a consistent data format due to real world variability. This lack of standardized data formats limits the number of standalone RPA solutions. By integrating RPA with intelligent document processing, enterprises can unlock and process data from a wide range of previously inaccessible documents (i.e. PDFs, images, emails, different structures in Excel files and more). Structured data packets can then be handled by downstream systems, achieving end-to-end automation.

Consider the real world application of automating an accounts payable function. Traditionally the Accounts Payable departments receive invoices from various vendors and suppliers, both electronically and physically. These invoices may need to be scanned and organized before key information, such as invoice number, the total amount due or a list of line items, can be extracted into the system of record for payment. Leveraging the latest in Machine Learning, intelligent document processing solutions can classify the pages as an invoice, locate the relevant fields, and automatically extract the information with a high degree of accuracy. Improvements in machine learning, Natural Language Processing and Optical Character Recognition (OCR) have made it possible to reliably extract data from any document type.

Machine Learning can take documents and turn them into data that is immediately useful and actionable by RPA. Coupled together, these two technologies have the ability to improve business processes, save time, and reduce costs, ultimately enhancing the overall customer experience. Unmand allows enterprises to achieve end-to-end automation through its all in one platform. The platform allows users to build, deploy and manage software robots, while complex unstructured documents can be intelligently handled.

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