Document Processing Automation
Successful business leaders understand the importance of delivering on customer needs today, while building the infrastructure and systems to be agile to meet tomorrow’s evolving requirements. Whether it’s being able to quickly respond to a customer inquiry, freeing up employee capacity, or reducing costly errors and rework, organizations are increasingly turning to automation to stay competitive.
Yet for all the focus on data, most enterprises today still struggle to unlock and use it at scale.
The first mile of data processing
With rapid advances in the areas of Artificial Intelligence [AI] and Machine Learning [ML], companies are comparing the effectiveness of their legacy systems and dated approaches to document processing automation.
Companies are turning to automation like never before to unlock and parse through data, gain efficiencies, improve customer and employee satisfaction, and ultimately drive business growth.
Remains Unnecessarily
Expensive
& Error-prone
This reliance on legacy tech products and manual processes pervades nearly every data-centric industry, from banking & financial services to insurance and the public sector, where medical or insurance claims, loan applications, account opening forms, tax documents and other information must be efficiently and effectively processed each day.
The key
question remains:
How do you
choose the
right solution
for your
organization?
This resource hub will help guide
you through the process of:
- Strategically outlining your business goals
- Selecting the right solution to meet and exceed your business needs
- Assessing the landscape of automated document processing solutions
Identifying Your
Business Goals
Increased
speed
Get back to customers faster and drive revenue.
Businesses that are highly automated are 6x as likely to see revenue growth of at least 15%.1
If your legacy back office takes days or weeks to process a car insurance or mortgage application, you risk losing customers – and market share – to competitors with modern systems that can respond instantly.
Intelligent automation reduces manual work and increases document processing throughput, so you can get back to customers faster and deliver a better customer experience.
Lower costs
& Risks
Become a more efficient and effective organization.
Office workers spend 69 days a year on administrative tasks, costing companies $5 trillion a year.2
Every hour operating in legacy tools and manual workflows is another chance for something to go wrong.
With intelligent automation software, your organization can decrease the direct, overhead costs associated with manual data entry, including reducing clerical errors at the source and the downstream time & expenses required to track down and fix them.
Greater
scale
Modernize your operations to handle unexpected challenges.
67% of respondents said implementing digital or software solutions would be important to remain competitive.
Don’t be held back by using legacy approaches that lock-in past workflows.2
Don’t be held back by using legacy approaches that lock-in past workflows.
Intelligent automation can help companies streamline their operations, eliminate cumbersome manual workarounds and cobbled-together legacy tech, and free up resources to deliver more innovative products and services that drive the business forward.
The Automated Document
Processing Solutions Landscape
There’s no shortage of new document processing automation offerings hitting the market, making it challenging to cut through the noise and feel confident selecting a solution that will add value now and into the future.
The key to successfully introducing automation into your organization is seeking the right opportunities to use integrated AI solutions to streamline critical business processes. Focus on solutions that can deliver on the potential of automation today and help you scale moving forward, tackling increasingly complex tasks.
To kick off your evaluation, we dive into three automation solutions below:
Optical
Character
Recognition
OCR is software that converts scanned images into machine-encoded text, typically transcribing it character by character. OCR uses rules- or template-based extraction, which requires users to train the system for each template type.
An offshoot of OCR is Intelligent Character Recognition [ICR]. ICR works like OCR, except with the purpose of capturing handwritten characters, one by one. ICR relies on a “constrained handprint” that separates handwritten characters into individual boxes. Most forms are not designed for ICR, making reliable, meaningful automation difficult.
Apply when
You have standard, high quality input documents to transcribe (e.g. machine-printed text, consistent handwriting in boxes, fields for extraction in the same location across pages).
Be mindful when
- You’re processing high volumes of real-world, diverse documents with handwritten, cursive and/or machine-printed text.
- You’re handling document imperfections including scan or fax distortions, white text on black backgrounds, or patterned or textured backgrounds. More often than not, OCR/ICR stumbles when faced with handwritten or cursive text and skewed, stretched or low resolution images, requiring human review and validation to check data accuracy.
- You’re plugging extracted data into downstream systems to unlock greater process automation. Character recognition is just one piece of the document processing puzzle, and OCR often leaves you with a text representation of the image but not the structured information needed for downstream processes. In addition, since OCR/ICR deliver low accuracy rates with extraction, this incorrect data can cause errors downstream.
Intelligent
Document
Processing
IDP software captures data from documents (e.g. text, PDFs, scanned images, emails), and categorizes and extracts relevant data for further processing using AI technologies, including computer vision, Machine/Deep Learning and Natural Language Processing [NLP]. Leading IDP solutions have technology baked into their offering to process scanned documents regardless of common imperfections, capture the data, and classify the data and document.
Apply when
- You’re interested in reducing the overall costs of processing huge volumes of data.
- You’re looking to process documents with greater speed and accuracy than traditional OCR.
- You’re looking for a software tool that is more resilient to changes in document templates and is built to handle diverse document types.
- You’re looking to streamline overall document processing workflows, improve workforce productivity, and enhance customer experience. IDP offers a wide variety of use cases across different industries and business functions, including client onboarding, record management compliance, claims processing, loan/mortgage applications and more.
- You’re looking to enable non-technical business users with a solution to increase document processing throughput, streamline operations, and provide greater visibility into operational performance and accuracy.
Be mindful when
- You’re evaluating vendors and choosing a solution. Intelligent Document Processing is a fast-growing market and not all solutions are created alike, so it’s important to put potential vendors through a thorough evaluation process. Critically assess both performance and deployment readiness. This means evaluating – and actually testing – how well the technology solution can solve certain document processing automation challenges like classification, extraction, and validation, as well as how easily the solution can be deployed and implemented into a production-ready environment.
- IDP vendors claim 100% automation or accuracy out-of-the-box. Leading IDP solutions will deliver high rates of accuracy and automation on Day 1, but they will also continue to get better over time. Find out if there are built-in mechanisms that ensure a highly accurate system at the start – one that becomes even more accurate over time due to advanced Machine Learning models.
- Vendors won’t discuss their underlying technology or allow you to test their offering in the real world. Plenty of vendors offer another “plug in whatever OCR engine you have” or utilize open source AI, but for long-term return, you want to invest in proprietary technology that delivers on both accuracy and automation.
- Vendor offerings don’t account for humans in the loop. People aren’t perfect, and neither are machines. Once you accept that, the key is to understand how a solution involves people “in the loop” to drive performance improvements. Look for an IDP solution with built-in quality checks that learns over time, driving lower error rates and higher automation, and has a friendly user interface that makes it easier for data keyers to work with the machine when necessary using the multi-tool and high-speed keyboard.
Questions to ask potential IDP vendors
The Hyperscience Difference
- Handwritten & machine typed text
- Data extraction quality
- Proprietary models
- Best-in-class extraction
Handwritten & machine typed text
We use a single extraction model for handwritten & machine typed text, versus most legacy solutions which require an operator to select which model to use, limiting scale and automation rates.
Data extraction quality
We’ve built separate models for each data type. Hyperscience knows that addresses usually have numbers upfront and street names toward the middle, so we read the page accordingly. This avoids ambiguity when the system thinks something should be a “5” vs. an “S” and improves data quality.
Proprietary models
We extract intent, not just characters. We’ve built and trained our proprietary models to interpret the intent of fields, so we can tell the difference between what is a response and what should be “dropped out” (like a field name or crossed out text).
Best-in-class extraction
We read outside the box. Humans don’t always follow instructions and “color inside the lines”, and our proprietary tech chases text outside the box to deliver best-in-class extraction. If we’re only seeing 8 digits of a Social Security Number, we’ll gradually expand the crop to find the 9th digit.
Robotic
Process
Automation
Gartner defines Robotic Process Automation [RPA] as a digital enablement technology that leverages a combination of user interface (UI) and surface-level features to create scripts that automate routine, predictable data transcription work. RPA provisions software agents – “bots” – that mimic human interactions with software systems, take on rote, predictable tasks, and act either in concert with humans (attended RPA) or mostly autonomously (unattended RPA).
Apply when
You have structured, text-based inputs and outputs with clearly-defined, repeatable manual steps to execute a particular business application.
You have processes that are:
- Rules-based
- Simple to moderately complex
- Stable
- Mature
- Documented
Be mindful when
- You’re dealing with unstructured document data or more dynamic text inputs, such as images and PDFs. While RPA can replicate human mouse clicks for simple tasks, such as dragging files into various folders or manipulating and updating data in a spreadsheet, it lacks the intelligence to handle variation or ambiguity. RPA does not classify or extract data from documents, but instead relies on complementary technologies like Intelligent Document Processing to lift and unlock the data.
- You’re looking to automate more complex workflows or upgrade your legacy tech systems. RPA automates processes as they exist today without taking into account whether the underlying system or process is flawed, binding enterprises tighter to their existing processes. Without realizing it, companies that invest in RPA are unable to make changes to their environment – like investing in a new third-party tool – without having people attending bots and tracking the systems, screens and fields that each automation touches because otherwise, the process would break.
- You’re looking to avoid add-on services like implementation or tying up significant resources in on-going technical maintenance.
Assessing Automated
Document Processing
Solutions
Data is the critical step zero of any business process, yet structured data represents a miniscule portion of an enterprise’s data stack.
Businesses today run on unstructured data, such as handwritten paper claims, PDFs, scanned images and more, that connects customers with organizations, different sections of a business with each other, and organizations with their partners. The variability, poor readability [messy handwriting, fax marks, low resolution] and lack of standardization of both paper and machine-generated forms make it near impossible for outdated rule-based systems to reliably and efficiently read and process these pages for downstream decision-making.
Intelligent Document Processing, which leverages AI and related technologies, has the intelligence to extract and classify increasingly diverse data inputs. Leading IDP solutions continue to learn on the data they’re exposed to, driving lower error rates and greater automation.
Selecting the Right
Solution to Exceed
Your Business Needs
Modernizing outdated back office processes with AI technology is how winning companies decrease wasted manual effort and increase output and productivity.
If you’re just getting started, focus on the business problems that need to be solved or the strategic goals you’re trying to achieve.
Your goals include finding ways to decrease costs, get more data, and drive increased revenue and profit. The right solution should provide clear metrics on how their offering will impact these KPIs. Work alongside vendors to help calculate all potential economic benefits of implementing their solution.