Get a demo
Select the type of demo you'd like to experience with Indico Data.
Select the type of demo you'd like to experience with Indico Data.
What is IDP for Insurance?
Insurance providers rely on vast amounts of documents and images for underwriting, servicing policies, processing claims, and adjudication. As part of their digital transformation efforts, they seek ways to automate these processes efficiently. Intelligent Document Processing (IDP) offers a solution that leverages artificial intelligence (AI) to transform unstructured data into actionable insights, enabling carriers to make better decisions. This results in reduced processing times, minimized errors, and improved customer satisfaction.
Many insurance documents contain unstructured data, posing challenges for traditional workflow automation approaches that depend on keywords, rule-based methods, and templates. IDP platforms use AI technologies to process unstructured data, making it possible to automate the intake and processing of insurance submission documents and claims. This brings immediate value to all types of insurance carriers, from auto and home to commercial and workersâ compensation.     Â
An AI-based platform can automate the insurance submission triage process. Instead of associates manually opening emails and attachments, intelligent document processing handles this task. The model can “read” emails, open attachments, categorize documents, and extract key data values, formatting them for entry into downstream underwriting systems.
By automating the submission process, insurers can handle more requests with the same workforce, reduce submission backlogs and underwriting leakage, and ultimately generate more revenue. This approach not only enhances operational efficiency but also improves overall decision-making capabilities within the insurance industry.
Grow property & casualty revenue without adding expense
Get work done faster, from underwriting to claims processing
Free up employee time for higher-value work
Codify and streamline property & casualty processes
With stalwarts and insurtech startups alike
Improve customer response time
Before AI and machine learning entered into the insurance industry, early attempts at automating insurance processes faced significant challenges, particularly in underwriting, claims processing, and policy servicing.
Underwriting and processing broker submissions before IDP for insurance
The commercial insurance underwriting process involves collecting various documents that describe the property in question, so the underwriter can accurately assign a value to it and calculate replacement costs. Early automation efforts relied on rule- and template-based approaches, which searched for specific keywords. However, this approach proved ineffective due to the unstructured nature of data in the underwriting process. Templates and rules work only on highly structured data, requiring data to be consistently placed from one document to the next. Given the variability and complexity of commercial insurance documents, this method fell short, leading to inaccurate and inefficient underwriting processes.
Claims processing before IDP for insurance
Claims processing in early automation attempts also faced hurdles. The process involves reviewing and validating numerous documents, including medical records, repair estimates, and legal paperwork. Using rule-based systems to identify and extract information was challenging due to the unstructured format of these documents. The inconsistency in document formats and the use of varied terminologies further complicated the extraction process. As a result, early automation tools struggled to handle the volume and diversity of claims documents, leading to delays and errors in claims adjudication.
Policy servicing before IDP for insurance
Early automation efforts also aimed to streamline policy renewals, endorsements, and customer inquiries, but the unstructured data in customer communications and policy documents made it difficult for these systems to function effectively. Templates could not handle the variability in document formats and customer requests, leading to frequent manual interventions. Consequently, early automation attempts failed to significantly improve efficiency or reduce the workload for policy servicing teams.
Optical character recognition (OCR) is another approach often touted as a P&C insurance process automation solution. OCR is a machine learning technology that can be used to convert documents such as PDFs into a machine-readable format. While thatâs useful, it doesnât address how to extract the relevant data.
Using robotic process automation (RPA) for P&C insurance likewise suffers from limitations when it comes to unstructured data. As its name implies, RPA uses software robots to repeatedly perform highly structured and repetitive tasks involving the same keystrokes.
But that’s not how the P&C underwriting process works. It requires a human to read documents and make judgments about which data to extract. Any other P&C insurance process involving unstructured data, which is most of them, will suffer the same fate regarding OCR and RPA. (RPA can complement intelligent document processing in insurance automation, more on that below).
Indico Dataâs approach to intelligent document processing in insurance is fundamentally different from RPA and templated approaches because Indico can understand document context much like a human does. Our unstructured data intake platform is built on top of a database containing 500 million labeled data points â enough to enable it to understand human language and context. It would take the largest P&C insurance carrier years to accumulate that much data and build its own model.
Automating the P&C insurance underwriting process requires reviewing numerous documents, with relevant data extracted and entered into a downstream processing system. An effective intelligent document processing solution automates processes by âreadingâ these documents much like a human would find the relevant data. It can also automate data extraction and data entry, saving an untold number of hours to dedicate to more valuable work.
Numerous processes may be involved in servicing a P&C insurance policy over its lifetime, including:
Many of these processes involve dealing with various forms of documentation, making them ripe for intelligent document processing.
The claims process is rife with documents coming in from various stakeholders, including customers, adjusters, brokers, appraisers and more. The documents may arrive via email, fax, websites, or traditional mail. Here again, the traditional manual process requires claims representatives to review the documents to find relevant data and manually enter it into the claims processing system.
The P&C claims process involves numerous steps including:
Many of these steps are transactional in nature, involving discrete steps and document reviews. Intelligent document processing for claims processing can help automate the review of a FNOL, including the document review process, extraction of key data points, and setting up the claim in a P&C claims management tool. It can also validate that all required data is present before sending the claim to an adjuster. Simple claims may be automated end-to-end based on pre-defined business rules, perhaps with only a final review and sign-off required at the end of the process.
The claim adjudication and subrogation process likewise consists of a number of steps that can be at least partially automated, including:
Applying intelligent document processing to claims processing can speed resolution of the claim, thus improving customer satisfaction.
For some P&C insurance automation use cases it may make sense to use robotic process automation to complement IDP.
RPA works well on processes that are highly deterministic in nature and involve structured data. In that sense, itâs well-suited to automating repetitive tasks, making a process less labor-intensive for humans. IDP, on the other hand, is able to automate processes that involve unstructured data.
A common IDP and RPA use case, then, is to use IDP to âreadâ unstructured data and translate it into a structured format before handling it off to an RPA tool. For example, the RPA tool may perform the initial document intake, then send the documents to an IDP tool for classification and data extraction. The IDP platform can then translate the extracted data into a structured format, such as a spreadsheet. The RPA tool can then take the now-structured data and automate the process of entering it into a downstream system, such as a claims processing system.
Itâs challenging for P&C providers to keep up with business requirements under the best of circumstances. But itâs imperative if firms are to achieve digital transformation. Indicoâs intelligent document processing platform helps you take a big step in the digital transformation journey while delivering significant benefits, including:
Automation enables P&C adjusters, appraisers, examiners, investigators and other employees to be more productive, enabling the company to increase revenue without adding headcount.
Automating P&C insurance processes empowers companies to get work done faster, even while increasing accuracy.
By automating mundane tasks, you can free up employee time for more rewarding work thatâs also more valuable for the company.
Part of the value of a P&C insurance process automation exercise is codifying processes that may have existed for years with no formal agreement on how they are supposed to work. Itâs also an opportunity to streamline processes to make them more effective.
Intelligent document processing ultimately makes your organization more competitive, putting you on equal footing with the most nimble insuretech startups and largest industry players alike.
Customer expectations are at an all-time high. Intelligent document processing allows P&C providers to exceed client demands by improving the speed and accuracy they can react to customer needs.
