The Indico Unstructured Data Platform complements your robotic process automation solution by automating processes involving unstructured data. Indico Data uses artificial intelligence technologies to digitize and extract data from unstructured data, which accounts for up to 85% of all data in most organizations, including PDFs, Word documents, emails, images, videos, and more. We call it Intelligent Process Automation (IPA). Combine Intelligent Process Automation with your robotic process automation platform, and you’ll automate tasks and workflows that include both structured and unstructured data. Think of it as intelligent RPA.
The key point is, it’s not IPA vs. RPA, but rather, IPA & RPA – complementary technologies, not competitive.
Automates deterministic, repetitive processes involving structured data
Has cognitive capabilities and automates processes involving unstructured data and documents, including images, PDFs, emails, Word documents, and more.
IPA ingests unstructured data, converts it to a structured format, feeds it back to an RPA platform.
in process cycle time
in process capacity
in resources required
When comparing IPA vs. RPA, it’s worth reviewing the technologies that IPA supports (and which RPA does not), including:
With these capabilities, an unstructured data automation platform can learn over time, gaining cognitive capabilities to handle some human-like decision-making applied to all sorts of document-based processes. For example, for insurance claims analysis, an unstructured data platform can quickly examine hundreds of claims and identify those that may indicate fraud. For titles and deeds, intelligent automation can extract the relevant data from these documents regardless of the varying formats between states and counties. An IPA platform can examine a set of RFPs and score them according to how well they meet business objectives.
In summary, customers should be thinking in terms of RPA and unstructured data automation as complementary. An intelligent automation platform translates the unstructured data into structured data to plug back into the RPA platform. Now you’ve got an intelligent RPA solution that can deliver unstructured data automation.
It’s a powerful combination that can apply to many common back-office use cases in insurance, including property and casualty and life insurance; financial services and commercial banking; commercial real estate; legal & compliance, sales & support, general operations, and more.
Robotic Process Automation Explained
RPA software involves automating repetitive tasks to make a process less labor-intensive for humans. It works well with deterministic business processes that involve structured data; in other words, where the process is exactly the same every time and where data is in well-defined fields, such as a spreadsheet. The process also must not require any human judgment, instead working strictly on “if/then” scenarios.
With RPA solutions, users program a software robot to follow the steps a human would normally take in performing a process. The robot performs tasks that are repetitive – and boring – for humans. By using computers to perform the tasks instead, RPA lowers costs and promises better accuracy, because computers don’t get tired or make keystroke errors.
When looking at business process management (BPM) vs RPA, there are some major differences. BPM is focused on improving an existing business process. That often involves automating some steps in the process, although that’s not necessarily a requirement. It’s more about optimizing a process to make it more effective and efficient, often by using methodologies such as Six Sigma and Lean.
RPA use cases for insurance include automating the claims process, such as inputting data from structured sources and ensuring all required fields are filled out. But insurance claims often include unstructured data, including photos showing auto damage, PDFs of scanned driver’s licenses, or images such as CT scans for a healthcare insurance claim. An IPA platform can extract unstructured data from PDFs and other sources, adding value to RPA in insurance.
For invoice processing, RPA automates data input, reconciliation error correction, and some decision-making. But the challenge is dealing with the many formats different vendors use for their invoices. That’s where an intelligent unstructured data platform contributes by creating an extraction model to pull out necessary data from the invoices, normalize it to a structured format, and send it back to the RPA platform for automated data input, error handling, and more.
RPA platforms can automate processes triggered by a specific event, such as the end of a contract period. A cable television company, for example, could use RPA to automatically send customers an email when their contract is nearing its expiration date, urging a renewal. With its ability to understand context, an unstructured data platform could go a step further, enabling companies to build models that review the customer’s current service lineup and activity throughout the year, such as movie rentals. This process can determine whether there’s an opportunity to upsell products or services.
Typically, companies have a central inbox that receives many emails from customers, contractors, suppliers, and the like, often with attachments. An RPA tool can detect when a new email arrives with an attachment, then automatically route the email to an intelligent automation platform. The IPA platform can then extract the attachment and “read” it, using OCR and NLP. It extracts relevant unstructured data such as payment terms, invoice numbers, and contractual language. The platform can then normalize the data appropriately and send it to a downstream system, such as customer relationship management (CRM) or enterprise resource planning (ERP) system.
Among RPA use cases in finance is dealing with all the data financial firms must compile for monthly and quarterly reports. RPA can aid in the process by automating data collection from various structured sources. But if you introduce an unstructured PDF document to the process, RPA hits its limit. Now you need the OCR and NLP capabilities from a financial services automation platform to pull out relevant information and convert it to a structured format that the RPA tool can handle.
Customer relationship management systems are valuable tools, helping sales stay on top of customers. But it’s also time-consuming for sales and marketing professionals to keep the CRM system up to date. RPA can help by automating the process of extracting customer data from invoices, purchase orders, and other systems and entering it into the CRM system. Here again, so long as the coming from and going to data fields are well-defined, RPA will be up to the task.
In a tech support scenario, RPA bots act as a first line of contact. They can help solve simple issues like password resets and diagnose problems by asking a series of questions. When issues need to escalate, the human support agent will have some preliminary information and get right to the job of diagnosing the problem and helping the user.
Example RPA finance use cases include aggregating data for financial reports, such as at the end of a quarter. So long as you know what reports the data is coming from and where in each report the data is located, RPA can automate the gathering and aggregation process and get it done far faster than a human. In banking, RPA can automate copying and pasting customer data from one banking system to the next. For credit analysis, RPA could automate the process of logging in to a credit bureau portal, uploading customer details, and downloading resulting credit reports.
Complementing RPA with unstructured data automation means you get all the business benefits that Intelligent Process Automation delivers, including:
85% reduction in process cycle times
Drive customer satisfaction and quicker time to market for new initiatives
4x increase in process capacity
Scale critical processes without increasing expenses, for more cost-efficient back office functionality
80% reduction in human resources
Free up critical resources to work on higher value-add projects rather than repetitive low-value tasks
1000x less training data required
As compared to traditional artificial intelligence solutions