Handling Unstructured Documents in Enterprises Using Intelligent Document Processing 

Business document processing is challenging in today’s workforce due to the enormous volume of documents generated and received daily by companies; however, today’s businesses should expect to receive many of these documents in non-standard or “dirty” formats. Business-critical information is usually contained within documents such as contracts, invoices, scanned forms, customer email correspondence, employee onboarding, compliance and regulatory records, and many other internal documents, and these documents will have varying structures, qualities, and layouts, which make it difficult for a traditional system to process this data. Therefore, document processing for unstructured documents is becoming more important than ever before for the modern-day business enterprise. 

When companies depend upon humans to review documents manually or use dated rules-based tools to process documents, it slows down the operation of processing documents (i.e., the time and cost of completing processing), while also leaving significant amounts of valuable information within the document instead of being easily processed into workflow or decision-making processes. Intelligent document processing utilises a combination of artificial intelligence (AI), optical character recognition (OCR, machine learning, and natural language understanding, to read and understand complex documents, extract data and to route data into processes/decision-making at an incredibly faster speed and with a much higher degree of accuracy than any other traditional method.

What Are Unstructured Documents?

There is no standard layout applied to an unstructured document at the time it is created, nor should it follow a pre-determined format, which would allow conventional software tools to easily understand it. Unstructured documents contain free-form text, various formats of layout, handwritten notes, reproductions and transcriptions, signatures, postmarks and stamps, links to outside material, and could also differ from one document to another regarding the number of pages or format. 

Documents filed as unstructured in the Enterprise could be considered to include some or all of the following: vendor contract, legal notice, email correspondence between customers, scanned proof of identity, invoice, onboarding forms for new employees, claim historical records, compliance documents, financial statements, service request, and ticketing systems. The two documents may differ completely in look based on the place they came from, what created the document, or the reason the document was created. 

This makes it much more challenging to manage an unstructured document than just keeping it on file. Businesses require systems capable of processing an unstructured document and also determining relevant information and the next appropriate action. 

Why Businesses Struggle with Unstructured Data 

Numerous companies recognise that there is a great deal of useful information contained within their documents, but the process of extracting and utilising that information can be much more challenging than anticipated. The challenge of processing unstructured data arises mainly from the fact that an enterprise’s documents are created in multiple departments, come from a variety of sources, and are interacted with by numerous outside parties, meaning they are not consistent; therefore, this causes delays. 

PDFs 

PDF documents are abundant in use by organisations. Common examples of PDF documents are contracts, statements, reports, invoices, purchase orders, applications, and declarations. While some PDF files can easily be processed, other documents present various challenges to standard methods for information extraction from document types. Some documents are text-based, while others are image-based. Some documents include table format, stamps, signatures, or multiple sections that span several pages, while others may lack formatting or contain embedded images. 

As a result of the various formats and styles of PDF documents, it can be difficult for standard tools to accurately detect and extract information from the documents. Because there is no defined reference point across documents and due dates, invoice numbers, client names, and contract clauses can appear in a variety of locations across documents, thereby causing issues for systems that are based on fixed templates. 

Emails 

Emails add to the complexity of enterprises. Critical business information is often difficult to find because it may be obscured by a long string of replies, email forwards, included web addresses that contain Cc: and Bcc:, attachments, and multiple approval levels. A single business request could be embedded in an email body, a PDF attachment, or a scanned document sent later. 

Typically, legacy systems do not know how to correlate these items. They can store text data, but they are unable to understand the actual business meaning of the data. Therefore, staff must spend time manually reviewing emails and attachments in order to determine what information is important, who needs to take action, and where the needed information should go. 

Scanned Docs 

Another source of escalation is scanning documents. The quality of the input can change from document to document. Most scans will be unclear, out of alignment, severely cropped (due to the angle at which the image was captured), handwritten, stamped, etc. Similarly, some scans will be aged, have stains, &/or tears that hinder machine reading. 

As businesses rely on people to manually read and re-key data from these documents, the time to process the information is significantly increased, and errors will be more abundant. The amount of time staff spend completing high-volume repetitive processes that can be automated takes away from being able to work more effectively. 

Limitations of Traditional Systems 

Conventional document and data retrieval systems were not created for today’s complicated enterprise documents. Most outdated systems were designed to work with documents that have a consistent form. They also rely on a lot of preconceived rules, fixed templates, keyword matching and manual tagging to function. 

The problem is, enterprise documents are rarely predictable. Vendors use different forms for invoices. Customers submit incomplete documents. Legal documents are changed in writing. Teams use different devices to scan documents. Conversations over email can vary greatly depending upon the sender, department or purpose of involvement. Thus, rule-based systems will become obsolete in these environments. 

Another major hindrance to the performance of traditional systems is that the scope of their analysis is typically focused on the words being read rather than the meaning of those words; while they may identify the presence of some numbers, they might not know if that number signifies the invoice total, due date, policy identifier, or tax amount. The lack of context leads to extraction errors and forces employees to manually review output. 

Most companies already use OCR software, but OCR alone does not solve this challenge. Reading words from a document is just one component of the equation. The bigger issue is identifying what those words mean in a business context. Without this intelligence, documents may be digitised, but they are not truly usable. 

How Intelligent Document Processing Solves This 

Document processing can be achieved by using Intelligent Document Processing (IDP), which leverages technology to read/calculate/understand/extract information from varied types of complex business documents. 

IDP uses artificial intelligence (AI) rather than simply relying on static rule sets or template-based methods alone, providing organisations the ability to process large numbers of documents with a high degree of manual effort or consistently yet more accurately. 

Context Understanding 

The single biggest advantage of IDP is the “context” the system uses when interpreting text-based content. The system will examine how different types of words relate to one another, how different parts of documents relate to one another, and how document layouts relate to one another. 

For instance, within the context of a given agreement, when interpreting a contract, IDP can recognise ‘Party Names’, ‘Obligations’, ‘Validity Periods’, ‘Renewal Clauses’, and ‘Payment Terms’ even if they are referred to differently across the various contracts. In financial documents, IDP can understand the difference between the ‘Invoice Date’, ‘Due Date(s)’, ‘Total(s)’, ‘Sales Tax’, and ‘Vendor References’. In operational documentation, IDP can interpret the difference between ‘Shipping ID’, ‘Customer Request’, ‘Approval Status’, or ‘Service Instruction’. 

Contextual awareness ultimately provides the opportunity for business and document AI to move toward a point of recognising the document electronically rather than simply capturing the text on a piece of paper. 

AI-Based Extraction 

Enterprises use AI-based extraction to get structured output from unstructured input, such that one system can learn how to process dissimilar files and find the required fields/products without having to be limited to static templates/forms. 

This has tremendous advantages in the area of unstructured data extraction for objects like invoices, contracts, KYC files, onboarding files, account statements, service forms, claim submissions, and attached emails. After extracting the needed data, those results can be inserted into business software tools (i.e., ERP, CRM, finance, compliance, and case management). 

With this solution, speedier turn times will be achieved, less time will be spent on manual entry of data, and data will be more consistent throughout the organisation. 

Continuous Learning 

Enterprise files are ever-changing because vendors are always changing formats, always updating the legal language used in documents, introducing new types of documents, and engaging with new partners or regulators. A static system cannot meet this high level of variability and therefore cannot support your ongoing process improvement initiatives. 

Continuous learning is an integral part of how today’s IDP platforms improve as users validate results, correct fields, or identify whether the document’s classification is correct or incorrect. Over time, IDP can identify patterns and learn about exceptions, successfully identifying new vendor files with unexpected differences from those previously identified while leveraging IDP’s ability to automate the identification of protected classes of individuals. 

This is why IDP (Intelligent Document Processing) for the unstructured document (document) space can provide your rapidly growing organisation with an unlimited, scalable solution to meet your current and future growth objectives. As discussed earlier, not only does IDP automate your present processes, but it continues to be more efficient as your organisation continues to expand.

Real Use Cases 

When you apply unstructured document processing to a real-life business process, things become much easier to understand. 

Legal 

For example, when legal teams are managing contracts, notices, amendments, compliance files, policy documents and case records, they are inundated with documents every day. Documents generally are text-heavy and have a diverse amount of formats (i.e. some are in columns, some are filled out using software, etc). As a result, the process of reviewing these types of documents takes a lot of time and also increases the chances of overlooking critical clauses and/or deadlines. 
Intelligent Document Processing (IDP) will help legal teams achieve the following: they will then be able to classify documents faster, extract important terms from those documents, identify any obligations from the contract (using natural language processing), track renewal dates and identify any relevant clauses for review. This reduced the amount of time spent reviewing the entire document and improved the lawyer’s or business’s visibility into each contract being reviewed. 

Finance 

Invoices, expense reports, bank statements, tax forms, vendor registration documents, payment confirmations, and approval emails are all documents handled by the finance department. These types of documents may come from many different external sources, and they may have different formats; therefore, handling these documents manually impedes the approval process by delaying approvals, reconciliation and reporting.  
Automating the capture of key fields, verifying the data, finding discrepancies and processing documents is done using IDP, which ultimately results in improved productivity, more accurate results, and improved financial processes for finance departments. 

Operations 

Many of the operational teams in an organisation rely on documents to perform procurement activities, logistics activities, onboarding new employees, providing services, supporting internal customers, and fulfilling customer service requests. Incoming documents, such as shipment records, vendor submissions, work orders, service request forms, and documents related to customer transactions, are all received from external sources in a variety of different formats that disrupt the smoothness of operating procedures. 
When document processing is automated for operational teams, such as with the use of AI-powered technology, team members can automatically classify the files received by their respective teams, extract necessary data from each file, automatically initiate/trigger follow-up actions using the data extracted from each file, and reduce the time it takes to receive a response between organisations. As a result, operational teams will achieve better internal coordination with one another and achieve faster response times. 

Business Impact

Intelligent document processing offers organisations much more than just the ability to extract data from documents. The real value comes from changing how businesses are run. 

First, it helps teams reduce the amount of time they spend performing manual tasks. By allowing teams to spend less time reviewing large numbers of similar documents, they can spend more time performing tasks that require judgment or that directly benefit the customer. Second, it increases throughput; the speed at which documents are classified, understood, extracted, and routed is greatly accelerated versus manual processing. Third, it enhances accuracy due to reduced human errors and inconsistencies associated with data entry. 

In addition, there is stronger compliance and audit readiness. Businesses can implement standardised and trackable document processing standards that provide them with greater control over how information is processed. This usually has a dramatic impact on organisations that operate in highly-regulated industries, where having accurate documentation is extremely important. 

Additionally, IDP increases scalability within an organisation. As the volume of documents continues to increase, organisations no longer need to grow their number of employees at a similar rate to meet the needs of processing and reviewing documents received into their business. This creates a much more attractive ROI for businesses that want to grow rapidly but efficiently.

Snoh Fusion for Unstructured Document Processing

Snoh Fusion is a platform powered by Artificial Intelligence (AI) that can automatically process unstructured documents found across enterprise environments where the layout, quality and format of those documents can vary greatly. Snoh Fusion allows enterprises to read, classify, understand and extract information from complex business files like PDFs, emails, scanned documents, contracts, forms, and attachments. 

Instead of considering these documents static records, Snoh Fusion changes them to usable business data that will enable faster and more accurate workflows. This will reduce the amount of manual labour done by an enterprise, improve consistency in document processing, and provide better control of operations that have a heavy reliance on documents. 

To further strengthen how documents are managed, Snoh Docs provides a single source of truth for all enterprise files and allows enterprises to store, organise, and access their enterprise files in one location. This reduces fragmentation and allows teams to have greater visibility into the documents that they rely upon daily. 

In addition to Snoh Docs, Snoh Flow helps leverage the value of document intelligence by allowing for process automation. Once documents are extracted and understood, they can automatically be moved into approvals, validations, finance operations, compliance workflows, requests for services, and any other downstream business processes. 

Together, these three components comprise a more robust foundation for the modern enterprise’s approach to document operations. 

See How Snoh Fusion Handles Unstructured Documents

Businesses can’t afford to have all their important information contained in different types of organising methods, including unorganised files, complicated email conversation threads, and improperly scanned records. Organisations must have unstructured document processing powered by artificial intelligence (AI) to allow for improved speed in workflow activity, cleaner data, enhanced decision-making, and reduced operating expenses. 

View the means through which Snoh Fusion processes unstructured documents and see specifically how automated intelligent technology could be utilised by your business as a means to translate complex document workflows into scalable, secure, and compatible with the business. 

Conclusion

The traditional methods of managing difficult business file formats require time and are prone to inconsistencies, in addition to an inability to meet future operational growth levels. Unstructured document processing with intelligent document processing technology should provide companies with the capability of extracting “real” value out of their unstructured content, such as PDF files, email messages, and scanned documents, on a more accurate basis and at less cost per application processed. By using solutions such as Snoh Fusion, the unstructured data created by companies will become usable, will support workflow improvements and additional operational efficiencies. 

Frequently Asked Questions (FAQs)

Related solutions: Explore Snoh Docs for intelligent document processing, Snoh Flow for workflow automation, and talk to our team for implementation.