Our previous blog covered the benefits and use cases provided by intelligent document processing (IDP), from invoice processing to accessing medical records and even extracting information from engineering drawings to benefit construction professionals. In part two, we’ll delve into its outstanding limitations and reveal how businesses can make the most of an IDP deployment.
IDP can handle a large majority of tasks that organizations don’t have the human resource in place to complete. However, out-of-the-box IDP licenses in isolation harbor a shameful secret. They are only capable of processing around 80% of unstructured documents without intervention by a human due to the limitations posed by machine learning (ML) technologies. Businesses require an IDP deployment with wrap-around support that opens the door to that much-needed 100% figure.
The Missing 20%
A 20% processing shortfall can bring inefficiencies and unnecessary costs to businesses. To ensure that an IDP deployment can perform to its full potential, expert specialists are the missing piece of the puzzle. The first step is to work closely with the business to put together a pipeline. Without a helping hand, Python programming skills, training, and practice are required internally, plus expertise with AWS or another cloud service.
Experts can help businesses build this pipeline by following a set of specific steps. The initial steps include defining each data attribute to be extracted, such as names or data types, determining the best data extractors to use to extract the attribute, and deducing validations to ensure the attribute was extracted correctly. The result is a real-time dashboard that provides information such as throughput, quality and human review task metrics, alarms, and the status of issues being worked on by the IDP support team. Legacy systems are also considered when integrating data into downstream systems and redesigning processes to ensure new deployments are maximized.
Keeping Humans Involved
A key aspect of this pipeline build is allowing humans to participate in the data extraction process. A Human In The Loop (HITL) interface allows a document review team to actively check and rectify any data extraction errors that make up the last 20% of unstructured documents. The technology works by presenting a human reviewer with a visual image of the document, as well as a correctable list of extracted data elements. The accuracy of the extraction may be questioned or it simply needs to be confirmed for validation purposes. This allows guaranteed accuracy and continuous improvement.
Honest expertise can advise businesses in cases where IDP in isolation cannot extract data, but the field of AI and natural language processing is also a fast-moving one. External teams are able to keep pace with these developments and incorporate new technologies where appropriate. Customers benefit from the latest solutions that both enhance efficiencies and save them the most money.
Unlocking the Full Value
More businesses are exploring how IDP can be used as a wider automation toolkit but aren’t making the most of its capabilities. Out-of-the-box IDP deployments fail to cover 20% of unstructured documents without human intervention, leading to inefficiency and unnecessary expenditure. Organizations can also suffer from creeping costs and underestimation of resources from attempting to implement IDP without the right assistance.
With BP3, businesses can bring down costs, human hours, risk, and errors, while helping to raise data security, compliance, and customer experience. We can manage the entire end-to-end process, and ensure the model is learning correctly by removing errors.
To find out more about the value of IDP and wrap-around support, read our full whitepaper for all the details.