Intelligent Document Processing Workflow : How does intelligent document processing work?

Data entry, collection, and documentation have never been particularly appealing tasks, much less one that is effective, accurate, or cost-efficient. According to IDC, 40% of surveyed employees spend between 21 and 30% of their workweek on duties involving documents. Businesses today understand the need for digital transformation to move more quickly, increase productivity and efficiency, and adjust to fluctuating market conditions, distributed workforces, and compliance rules. 

Further, businesses have to deal with the data-automation conundrum which is massive volumes of unstructured data. As a result, businesses are scrambling to digitize data and implement automation technologies like intelligent document processing (IDP) workflows to advance their transformational requirements.

Let’s see why modern businesses need IDP processing, the technologies powering intelligent document processing workflows, IDP workflow benefits and challenges, and its real-time use cases.

What is Intelligent Document Processing (IDP)?

Company data is the driving force behind digital transformation, yet 80% of the available data is in unstructured formats such as business documents, emails, photos, and PDF files. 

IDP is an automation technology that automates data capture from multiple documents and data sources and organizes it for future processing. Using cognitive technologies like natural language processing (NLP), computer vision, deep learning, and machine learning (ML), IDP workflow allows businesses to automatically identify, categorize, and process relevant information from unstructured, semi-structured, and structured documents. 

Intelligent document processing workflow, in other words, extracts data from documents and converts it into process-ready and usable information.

How does Intelligent Document Processing work? 

IDP is a data extraction technology worth exploring for businesses with a significant volume of organized, semi-structured, or unstructured data. The Intelligent Document Processing workflow involves six steps:-

Step 1: Document pre-processing 

The initial stage in intelligent document processing is collecting data from many sources. IDP workflow uses a process known as "data capture" to consume data. Document pre-processing occurs as soon as a document is fed into a document processing system (IDP workflow). Some basic techniques employed in this phase are binarization, deskewing, and noise removal.

Step 2: Document classification

• Before analyzing the content in between, document classification first identifies the beginning and ends of the source material. Classifying document types, such as invoices, purchase orders, identity documents, contracts, bills, and insurance claims among many others fall under intelligent document processing.
• IDP workflow uses OCR technology to analyze the data if the source is a PDF document or a scanned image of a document. It does this by extracting characters, numbers, and symbols from the data it scans.

Step 3: Data extraction

• The most crucial stage of intelligent document processing, after file type classification and data source format analysis, is data extraction.
• Intelligent document processing deploys trained AI models that use deep learning (DL), machine learning (ML), and natural language processing (NLP) methods to extract useful context from the source.
• Document extraction focuses on specific aspects of interest, such as addresses, tax information, monetary values, and product technical specifications among many others. 
• The data is subsequently entered into a database or stored for later use through intelligent document processing.
• The data then flows into a variety of business application databases. Examples of database applications include spreadsheets, accounting software, enterprise resource planning (ERP) software, enterprise content management (ECM) software, and customer relationship management (CRM) software.

#Step 4: Data validation 

IDP validates extracted data against business different rules, comparisons, and unstructured/structured data. For example, comparing all the addresses on utility bills and bank records with those collected from application forms for applicants. Another example would be verifying that invoice totals are accurate by comparing information from related purchase orders.

While the validated data goes to third-party apps for further processing, the data that fails validation is sent for correction. 

#Step 5: Data analysis

Decision makers use the insights generated by the IDP software to improve business processes. Data analysis gives insights into error rates, and document processing times, and normalizes data for easy consumption. 

#Step 6: IDP workflow integration and human review 

A human-in-the-loop review improves data accuracy. This is beneficial for the model's supervised learning process and for increasing the model's reliability. The final step of intelligent document processing is exporting the information to internal data systems and integrating other business process workflows. 

The benefits of adopting IDP for business operations 

Organizations are overwhelmed by documents that need to be processed. The potential impact of IDP processing is immense across industries and business functions. The advantages of using IDP workflow are: 

#1. More than 99% accurate data extraction 

By eliminating data entry errors, the intelligent document processing software has more than 99% data extraction accuracy. Regardless of the volume of documents, the same quality of output is maintained throughout the data extraction process which can be done 24/7 and reduces the wastage of human resources for data preparation. 

Advanced IDP software like Docsumo can flag data entry errors to ensure highly-accurate document processing. 

#2. Reduces operational cost by 60-70% 

Unlike human resources, intelligent document processing workflow eliminates duplicate entries and does not need human intervention. AI-led models and APIs can be trained to send automated alerts in case of duplicate entries and fraudulent payments. Being cloud-based, Docsumo stores data digitally, reducing the cost of archiving data physically. 

#3. Improves efficiency by 10X by integrating with existing business systems 

Automated document processing ingests, categorizes, and classifies data within seconds without manual intervention. The IDP processes unstructured, semi-structured, and structured documents in a variety of formats at scale. 

By integrating with third-party software, data for processing flows between systems and does not need humans to feed information to the IDP workflow. After processing, the data is sent to downstream applications for further automation.  

#4. Reduces the processing time to 30 seconds 

The intelligent document processing software serves as a single source of truth for extracting and processing data within 30-60 seconds. Organizations save resources on data input and the IDP makes data more accessible and allows businesses to establish a highly efficient digital workforce. 

Since all the information is stored over the cloud, IDP improves cross-departmental collaboration and saves the employees from the grunt work of manually collecting data, organizing it, and cleaning it for further processing. 

Challenges businesses face in implementing IDP 

Despite the far-reaching advantages of IDP workflows, implementing intelligent document processing technology can be riddled with challenges despite the size of the organization or technical expertise of the staff. The most common IDP implementation challenges are: 

#1. Resistance to change 

Automation in the workplace can often lead to resistance and resentment. Before implementing an intelligent document processing workflow, get the buy-in from teams like accounting who’d be using the platform daily. 

Educating employees on how IDP processing technology improves their productivity so that they can focus on revenue-generating tasks and not challenge their jobs is a great way to get started. 

#2. Integration issues in IDP workflow 

The IDP processing platform should be able to extract data from documents and send it to downstream systems for further processing or storage. 

#3. Compatibility with the business/industry

The intelligent document processing software should be compatible with your legacy systems and be able to extract data from the documents widely used. For example, IDP for logistics involves extracting data from bills of lading, dock and warehouse receipt, insurance certificates, and more.

#4. Perception of cost

Perception of cost is a significant barrier for businesses while implementing automated document processing. Businesses may perceive the cost of IDP solutions to be too high, and therefore hesitate to invest in the technology. Some of these costs include:

Upfront Costs

One of the biggest perceived barriers to implementing IDP is the upfront cost of purchasing and implementing the technology. Businesses may be hesitant to invest in a new system if they perceive the initial cost to be too high.

Ongoing Costs

Another concern for businesses is the ongoing costs of maintaining and upgrading the system. This includes the cost of software upgrades, hardware maintenance, and ongoing support.

Integration Costs

The cost of integrating IDP with existing systems and processes can be high. This can be a complex and time-consuming process, and may require additional resources and expertise.

Training Costs

Implementing IDP also requires training employees on how to use the technology effectively. This can be a significant cost for businesses, both in terms of time and resources.

ROI

Finally, businesses may be hesitant to invest in IDP if they are unsure of the return on investment (ROI) they can expect. While IDP can improve efficiency and accuracy, businesses may not see the immediate financial benefits of the technology.

#5. Compliance

Does the IDP solution pass the security requirements your particular country has set? For example, for companies dealing with US and Europe-based customers, GDPR compliance is a must. Also, enterprise organizations may not permit integration with IDP processing solutions that are not GDPR, ISO- or SOC2-compliant.

Successful use cases of IDP processing from businesses around the world

According to IDC research, 90% or more of unstructured data is never processed, missing out on potential value and possibly jeopardizing compliance with data protection rules. The proven and widely accepted use cases of IDP workflows include:  

Financial Services

Financial services companies use IDP processing to analyze and extract information to ramp up processes including processing bank forms, conducting due diligence, reviewing credit applications, and onboarding new customers, with a focus on time and resource savings.

Let’s take an example of loan application processing which involves extracting data from account statements, bank statements, and identity verification documents, and structuring it in a way that it becomes easy to understand the cash flows of the account. This helps financial and banking organizations verify proof of employment and assess the creditworthiness of the individual before approving their loan applications. 

Insurance

Insurance firms rely on IDP to process papers for claims intake, loss notification, and loss estimations since IDP handles large volumes of documents. As the IDP workflow can handle document layouts, content, and formats in insurance documents including quotations, binders, and ACORD forms, it represents a significant advancement in document processing for the insurance sector.

Here’s how IDP workflows for insurance streamline the data extraction process for claim processing. IDP software can automatically ingest, classify, and compile user-defined data from different sources such as emails, scanned documents, and online forms. For an industry like insurance that requires high accuracy data and a single error can lead to massive loss of time and resources, IDP drives accuracy in the whole process.  

Transportation and Logistics

IDP's speed and accuracy benefit processes that depend on the swift and correct transfer of information from documents into logistics systems, such as invoices, bills of lading, and delivery notes.

Let’s take the use case of how BiagiBros uses intelligent document processing to fuel supply chain management. A 3PL warehousing company BiagiBros handles more than 1500 monthly deliveries across distribution centers, warehouses, and truck terminals throughout the US. 

Without automation, the challenges were: 

• Managing more than 50 inbounds daily with drivers delivering goods into their warehouses. 
• Most customers couldn’t generate barcodes or send bills of lading with the drivers. This resulted in data scanning and extraction failures on a large scale. 
• Manually scanning the bills of lading, extracting data, and generating barcodes for each customer took nearly 30 minutes. 

Docsumo’s IDP processing workflow brought down the time for barcode generation and data scanning from 30 minutes to under 2 minutes. Through data extraction, categorization, and validation process, Docsumo integrated this data into meaningful datasets in the supply chain process. 

CRE lending and underwriting 

Digitized CRE underwriting and servicing leads using the IDP processing platform Docsumo lead to 10X higher efficiency for financial spreading, income verification, and insurance compliance. Pre-trained APIs can classify documents, identify key points, and validate data, resulting in a 95% STP (straight-through-processing) rate. 

Intelligent document processing workflows for CRE lending include: 

• Generating key metrics such as net operating income and occupancy percentage and verifying the accuracy of data including lease starts and end dates for faster decision-making within 30-60 seconds. 
• CRE insurance compliance automation by Docsumo goes beyond template-OCR to extract data from unstructured emails and other submission documents, resulting in improved transparency and compliance. 
• Commercial real estate asset management - For financial spreading of the loan portfolio, lending organizations can categorize line items, calculate ratios, and validate totals in financial statements to streamline data validation for asset management teams. 

Legal 

Among unstructured corporate data, legal documents are among the most complicated. Corporate legal teams use IDP workflow to identify the most important information in unstructured legal documents, such as contracts, that would normally take hours of expert resource time to complete.

Intelligent document processing workflow automates ingesting, processing, and analysis of legal documents, regardless of the format. 

Concluding: Get started with IDP 

The promise of saving time, costs, and efforts while enabling automation at scale has led to the widespread adoption of intelligent document processing platforms like Docsumo. 

Start implementing IDP workflows for data extraction by signing up for a free Docsumo trial.