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Indicoās Intelligent Process Automation solution complements yourĀ Robotic Process AutomationĀ platform by automating your processes involving unstructured content. Intelligent automation effectively digitizes and extracts unstructured data, which accounts for up to 80% of data that needs processing, including PDFs, Word documents, emails, images, videos, and more. Combine IPA with your robotic process automation platform, and you’ll automate tasks and workflows that include both structured and unstructured content. In short, itās not IPA vs. RPA, but rather, IPA & RPA ā complementary technologies, not competitive.
Automates deterministic, repetitive processes involving structured data.
Has cognitive capabilities and automates processes involving unstructured 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 tool.
Process Cycle Time
Process Capacity
Resources Required
Intelligent Process AutomationĀ builds on the AI concept of transfer learning, enabling a model trained on one task to perform another related task.
When comparing IPA vs. RPA, itās worth reviewing the technologies that IPA supports (and which RPA does not), including:
Ā· Machine learning (ML) and deep learning models classify and extracts documents and perform software training.
Ā· Optical character recognition (OCR)/Intelligent Character Recognition (ICR) converts document images into machine-coded text, using ML and deep learning algorithms to train for increased accuracy.
Ā· Natural Language Processing (NLP) analyzes text in documents, understands surrounding context, consolidates extracted data, and maps the extracted fields to a defined taxonomy.
These capabilities enable IPA solutions to learn over time and give cognitive capabilities to handle some human-like decision-making applied to all sorts of document-based processes. For example, forĀ insurance claims analysis, IPA solutions quickly examine hundreds of claims and identify those that may indicateĀ fraud. For titles and deeds, intelligent automation can extract the relevant content 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 not be thinking in terms of intelligent automation vs robotic process automation. IPA translates theĀ unstructured contentĀ into into structured data to plug back into the RPA platform. IPA/RPA combinations apply to many common back-office use cases in insurance, includingĀ property and casualtyĀ andĀ life; financial services and commercial banking; commercial real estate; legal & compliance, sales & support, general operations, and more.
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 the IPA 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.
Financial firms must compile lots of data 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 and RPA hits its limit. Now you need the OCR and NLP capabilities from an IPA solution to pull out relevant information and convert it to a structured format that the RPA tool can handle.
Insurance companies can automate the claims process with RPA platforms, 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 relevant information from these sources, adding value to RPA in insurance.
Typically companies have a central inbox that receives many emails from customers, contractors, suppliers, and the like, often with attachments. RPA can detect when a new email arrives with an attachment, then automatically route the email to an intelligent automation tool. The IPA tool can then extract the attachment and āreadā it, using OCR and NLP. It extracts relevant unstructured content such as payment terms, invoice numbers, contractual language, etc. The tool can then normalize the data appropriately and send it to a downstream platform, such as customer relationship management (CRM) or enterprise resource planning (ERP).
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. But with its ability to understand context, an IPA tool reviews 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.
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 up to date. RPA can help by automating extracting customer data from invoices, purchase orders, and other systems and entering it into the CRM system. Here again, so long as the data fields are coming from and going to 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 wherein each report it’s 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.
Drive customer satisfaction and quicker time to market for new initiatives
Scale critical processes without increasing expenses, for more cost-efficient back office functionality
Free up critical resources to work on higher value-add projects rather than repetitive low-value tasks
As compared to traditional artificial intelligence solutions
