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Documents that contain unstructured content present problems for most insurance companies’ insurance policy automation solutions because they are not easily digestible by computers.
Claims processing, for example, often involves a review of notes from an adjuster based on conversations with the claimant. The adjuster’s notes are primarily free-form, following no standard format, with plenty of variation from one adjuster to the next. An adjuster’s file may also include photos, along with reports from doctors and lawyers. In short, it’s a significant amount of content, nearly all of it unstructured – and hence a prime candidate for claims processing automation.
To process a claim, someone has to pore through hundreds of pages of documents, extract pertinent bits of information and input them into a downstream claims processing system. Relevant data may include the claim number, policy number, date and time of loss, location, coverage limits, and more.
That data entry job is labor-intensive and time-consuming, not to mention error-prone, making it ripe for insurance document automation. Companies have tried using insurance process automation tools based on keywords and rules, with less-than-stellar results.
Keyword and rule-based approaches to insurance automation use templates that define precisely where the data you want to extract is located in a given document, along with a slew of rules defining what data to extract and what to do with it.
In practice, an insurer often hires a consulting firm to write countless rules and templates to try to account for every variation in the documents the company needs to process. That may work, briefly – until a document comes along that doesn’t neatly fit into any of the rules or templates the consultants created. Once this happens, the entire system breaks down – and that’s why you need to deploy insurance intelligent automation.
If you think of insurance adjuster’s notes, it’s easy to see how it won’t take long at all until a new type of document comes along, making the rule-based approach all but futile. On top of that, it’s horrifically costly, whether you use outside consultants or in-house resources.
It’s not unusual to hear about optical character recognition (OCR) as a solution for intelligent document processing in insurance claims and other processes. But by itself, OCR can’t effectively deal with the unstructured content that is the hallmark of insurance documents.
OCR is machine learning technology that can convert documents such as PDFs into a machine-readable format. That’s useful, but it still leaves you dealing with templates to extract useful information and all the issues that still exist.
Robotic process automation in insurance suffers from much the same problem when it comes to these types of use cases and insurance workflow automation in general. It’s why we often hear that RPA alone falls short with certain insurance use cases such as automating claims processes.
There are a number of RPA use cases in insurance. RPA is great at automating processes that involve the exact same steps each time. Say, for instance, an insurance data entry clerk entered the exact same keystrokes in the same order time after time into a claims processing system. That would be a process that’s ripe for RPA.
But, as explained above, that’s not at all how the process works. Rather, it requires a human being to make judgment calls about which data to extract and enter. Any insurance process that involves unstructured documents – which is most of them – will suffer the same problem. (RPA can, however, complement IPA in an insurance automation solution. More on that here.)
As implemented by Indico, intelligent document processing technology is fundamentally different from RPA and templated approaches because IPA can understand document context much like a human does. That’s because it’s based on a model that incorporates some 500 million labeled data points, enough to enable it to understand human language and context.
Having a large set of data to “train” brings intelligence to any artificial intelligence solution. But to utilize AI in insurance, even the largest insurance companies would be hard-pressed to create their own model based on that much data.
Indico then applies technology known as transfer learning to create custom models that can tackle virtually any downstream task – including claims automation and other common insurance use cases. The result is insurance companies can automate processes using a relatively small number of documents to train the model. What’s more, you don’t need data scientists to make it all work. Rather, the professionals on the front lines train the model – those who know the processes best. (For a deeper dive, check out our Intelligent Process Automation page.)
Insurance claims automation is another common use case. Intelligent automation for insurance can automatically classify and annotate a new claim and routed to the right SME for evaluation and processing. This results in faster turnaround time and improved accuracy for a processed claim, driving improved customer satisfaction and organizational efficiency.
Whether performed before writing a policy to determine property value or after a claim to determine compensation, insurance appraisal processes can involve many unstructured documents. An initial appraisal for property value may include receipts, purchase and sale agreements, and images, while claims have contractor estimates and more. An IPA solution can deal with each type of document and help companies automate the extraction of relevant data. This offers another opportunity for insurance document automation.
Often involving thousands of pages of documentation, major commercial underwriting processes can be dramatically improved by creating underwriting criteria attributes. These attributes are automatically recognized and “scored” using Intelligent Automation, resulting in a major reduction in response times when submitting proposals. This also consists of use cases such as loss run reporting and the like. One of the thorniest parts of the commercial insurance underwriting process is getting an accurate picture of the applicant’s loss history, generally gleaned from loss run reports. But it can be cumbersome to collect all the reports and accurately extract data from them for input into the underwriting system, making it an excellent candidate for intelligent automation in insurance.
In a highly regulated industry with dozens of state and federal regulatory bodies, promptly responding to regulatory inquiries represents a significant expense for most insurance companies. Intelligent Process Automation can create augmented responses to inquiries, dramatically reducing the response times and resources required.
Few vertical industries are as document-intensive as healthcare, whether on the provider or insurance side. That makes the healthcare industry ripe for tools that automate insurance claims processing and other chores for providers and insurers alike. Intelligent process automation can help healthcare organizations address unstructured documents driving cost savings and improving the patient experience.
Getting new clients is a good thing, but it also creates a challenge for insurance companies: processing all the required documents. To date, it’s been a largely manual process that for large insurers can easily involve 15 million documents per year, making it a ripe target for intelligent document processing technology.
RPA is great at automating repetitive tasks to make a process less labor-intensive for humans and works well with deterministic business processes that involve structured data. IPA, on the other hand, can automate processes that involve unstructured data.
A common IPA and RPA use case, then, is to use IPA to “read” unstructured content and translate it into a structured format that an RPA tool can then process. For example, the RPA ingests documents and send them to IPA solution for classification and data extraction. Once extracted, the IPA tool puts the data into a structured format that the RPA tool can work with, such as a spreadsheet. The RPA tool can then take in the now-structured data and populate a downstream system, such as a customer relationship management (CRM) tool.
Keeping up with the pace of business is difficult under any circumstances. But if insurance companies are to achieve digital transformation, it’s imperative. Indico’s intelligent document processing solution can help in that effort while delivering significant benefits, including:
Automation empowers employees to be more productive, so the organization can grow revenue without the expense of increasing headcount.
Automating manual processes allows companies to get work done faster, even while increasing accuracy.
By automating mundane tasks, you can free up employee time for more rewarding work that’s also more valuable for the company.
Part of the value of an automation exercise is codifying processes that may have existed for years with no formal agreement on how they are supposed to work. At the same time, you can streamline processes to make them more effective.
It all adds up to making your organization more competitive, putting you on equal footing with the most nimble insurtech startup and largest industry player alike.
Customer expectations are at an all-time high. Intelligent automation provides insurers to exceed client demands by improving the speed and accuracy by which they are able to react to customer needs.