Understanding Intelligent Automation

Understanding Intelligent Automation and its Key Benefits

How to bring artificial intelligence technology to bear on processes involving unstructured content – and reap big business benefits.

Learn how Intelligent Automation allows organizations to automate their most complex document-based workflows without the need for templates, rule-engines or data science expertise.

Key Benefits of Intelligent Automation

Empower Process Owners

Intuitive point and click interface for unstructured content classification, extraction, and workflows; no data scientists required.

Move Beyond Robotic Process Automation

Go beyond RPA and automate your most complex unstructured document-based workflows – with no need for templates or rules.

Reduce Process Cycle Times

Realize an 85% reduction in process cycle times while increasing process capacity by 4x and reducing human resources required by 80%.

What is Intelligent Automation?

Enabling organizations to automate their most complex document-based workflows

Intelligent automation software enables companies to automate processes that include unstructured content, including emails, PDFs, Word documents, images and more. Typically, at least 80% of all content in an organization is unstructured.

Robotic process automation (RPA) vs. intelligent automation

That’s a problem for organizations that are counting on robotic process automation (RPA) to automate document-intensive processes. RPA is good for automating highly repetitive processes that are performed the same way time after time. But it can only deal with processes involving highly structured data, so the software robot knows exactly where to find the data it needs to process. The same is true of templated approaches to automation that use optical character recognition.

Some RPA vendors claim to incorporate AI technology to automate all kinds of processes. But if you look closely and ask the right questions, you’ll more than likely find the solution is based on a rules engine that can’t effectively deal with unstructured content. Such “RPA + AI” solutions will help you only with structured and, perhaps, semi-structured use cases such as automated invoice processing.

An effective enterprise intelligent automation platform is built on a massive database of labeled data points. These data points give the tool context behind virtually any type of document or content. In essence, it enables the tool to “read” and understand unstructured content much like a human would.

Taking advantage of AI technology called transfer learning – which enables a tool trained on one task to be used for another, related task – users can then train the intelligent automation tool to automate any document process, including those that involve unstructured content.

Intelligent automation - the Indico way

Indico’s approach to intelligent automation, based on its Intelligent Process Automation platform, takes advantage of a base model consisting of more than 500 million labeled data points. To automate a given process, users label some of the actual documents involved in the process, using Indico’s intuitive tools to detail exactly what type of data to look for.

This is not templating, however. Rather, thanks to transfer learning and that massive base model, the Indico platform can actually understand the type of data you want to extract – a social security number, for example – and find it wherever on the document it may be.

It takes only a few hours and maybe 200 documents to train a model that will be about 95% accurate. That’s a tiny fraction of the amount of data required for traditional AI approaches – 100x to 1000x less.

Create Citizen Data Scientists

With Indico, the business subject matter experts who “own” and understand the document processes are the ones who actually build the automation models. We turn these business people into “citizen data scientists.”

This is a far faster and more effective approach than having to explain business requirements to an IT or data science group, then hope they grasp and capture them effectively. Indico tools are so simple to use, users can build working models in an afternoon.

Sound Intelligent Automation Requires Effective AI

While simple to use, Indico’s intelligent automation platform is built on sophisticated AI technologies, including deep learning, machine learning (ML) and natural language processing (NLP).

With deep learning and machine learning, you no longer have to write extensive programs in order to tell the model what to do. Instead, you provide examples of what you want the end result to be and the tool figures out how to provide it.

NLP, meanwhile, enables the Indico platform to understand context behind any kind of content, unstructured or structured. The tool can “read” documents and scan images much like a human does, looking for the sorts of data it has been trained to find.

Typically, AI products that incorporate deep learning, ML and NLP to address process automation require data science expertise to implement, which is scarce and expensive. Indico builds in sophisticated AI but keeps it under the covers and puts a simple user interface on top, making the technology accessible to everyday business professionals.

Intelligent Automation Use Cases in Financial Services

Mortgage underwriting

The mortgage underwriting process typically involves humans looking over lots of documents to assess an applicant’s creditworthiness. Applying intelligent automation to mortgage underwriting automates the process by “reading” the documents and extracting relevant data for input into the bank’s credit evaluation system.

Customer onboarding

IDP can take the various documents required to onboard a new customer and automatically classify them, extract relevant data and input it into the bank’s digital management system. Customers are onboarded more quickly, with increased accuracy, resulting in faster time to revenue for the bank and improved customer satisfaction.

Automate LIBOR document processing

The LIBOR interest rate benchmark is due to be phased out at the end of 2021. Banks and financial institutions worldwide are left poring through documents looking for references to LIBOR in order to determine what their exposure is and take steps to address it – a task that screams for IDP.

Intelligent Automation Use Cases in Insurance

Life insurance underwriting

Applying intelligent document processing to life insurance underwriting can help companies dramatically improve the process by largely taking humans out of the equation. With IDP, companies can create models to quickly categorize and extract data from reams of documents.

Claims processing

For insurance claims processing, intelligent document processing can be used to automate the classification and annotation of new claims, and route them to the appropriate subject matter expert for processing. It can also help extract pertinent information from documents, including unstructured content such as images and free-form notes from insurance adjusters.

Other Use Cases for Intelligent Automation

Invoice Processing

Most any big company must process large numbers of invoices, making it a process that’s a prime candidate for automation. While some may try to apply RPA or templated approaches, when you’re dealing with invoices in many different formats from lots of different companies, you’ll quickly hit a wall. By taking advantage of AI technologies including ML and NLP, intelligent automation tools can handle unstructured content and determine the relevant information that needs to be extracted from each invoice – no templates required.

Corporate email inbox automation

Corporate email in-boxes can quickly become overrun, making it difficult for humans to keep up with the volume. An intelligent automation platform can largely take humans out of the loop. Companies can create models that enable the tool to “read” each incoming email, determine what the topic is, and forward it to an appropriate department or subject matter expert. For some simple requests, such as a change of address, the intelligent automation tool could extract the pertinent information and input it into an appropriate downstream system. Models can also be built to handle email attachments, including reading and categorizing them as well as extracting relevant data for input into customer relationship management (CRM) and other systems.

The Business Benefits of Intelligent Automation

Bringing transformational efficiencies to the enterprise

It’s clear from use cases like those above that intelligent automation offers significant business benefits. Indico’s customers are experiencing benefits that are hard to overstate, including:

85% reduction in process cycle times

Realize faster time to market for new initiatives and improve customer satisfaction.

4x increase in process capacity

Create dramatic cost efficiencies for back-office functions and scale critical processes without increasing expenses.

80% reduction in human resources

Free up employees from tedious, low-value tasks and repurpose them for higher value, more strategic projects.

Ease of use

With no data science expertise required, turn your business process experts into citizen data scientists.

1000x less training data required

Build effective models with a fraction of the data traditional artificial intelligence solutions require.

Built for unstructured content

Automate your most complex document-based workflows.

Calculating the ROI of Intelligent Automation

Learn Why MetLife Expects to Save $100M with Intelligent Automation

Figures like a 4x increase in process capacity and 85% reduction in cycle times may sound almost too good to be true. But they’re based on real results from real customers, who report a rapid return-on-investment (ROI).

One such customer is MetLife, whose VP of Intelligent Automation recently discussed his automation journey with Indico’s CEO, Tom Wilde. Watch the full video to learn about MetLife’s journey from automating simple tasks with RPA to using intelligent automation to automate numerous unstructured document-based processes. You’ll learn how, over the next 5 years, MetLife expects to capture $100M in value by automating processes involving unstructured data.

 

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